Biomarkers for use in prognosis of mortality in critically ill patients

ABSTRACT

Biomarkers and methods of using them for aiding diagnosis, prognosis, and treatment of critically ill patients are disclosed. In particular, the invention relates to the use of biomarkers for prognosis of mortality in critically ill patients with sepsis, severe trauma, or burns.

CROSS-REFERENCING

This application is a § 371 national phase of International ApplicationNo. PCT/US2017/029468, filed on Apr. 25, 2017, which claims the benefitof U.S. Provisional Application Ser. No. 62/354,789, filed on Jun. 26,2016, which applications are incorporated by reference herein.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under contractsAI057229, AI089859, AI109662, and AI117925 awarded by the NationalInstitutes of Health. The Government has certain rights in theinvention.

TECHNICAL FIELD

The present invention pertains generally to methods for prognosis ofmortality risk in critically ill patients. In particular, the inventionrelates to the use of biomarkers that can be used for prognosis ofmortality risk in critically ill patients with sepsis, severe trauma, orburns.

BACKGROUND

Sepsis, newly defined as organ failure caused by systemic response toinfection¹, contributes to half of all in-hospital deaths in the US, andis also the number one overall cost to the US healthcare system.^(2,3)Although sepsis outcomes have improved over the last decade withimprovement in standardized sepsis care, mortality rates remain high(10-35%)⁴. Sepsis treatment still consists of source control,antibiotics, and supportive care. Despite dozens of clinical trials forimmune-modulating intervention, no treatments specific for sepsis havebeen successfully brought to market⁵. Two consensus papers have arguedthat the failure of clinical trials is due to the massive patientheterogeneity in the sepsis syndrome, and our lack of tools for accuratemolecular profiling^(5,6). Current tools, mainly clinical severityscores such as APACHE and SOFA, and the blood lactate level, are notreadouts of the underlying inflammation in sepsis, but rather a crudelook at the global level of patient illness.

Several groups have hypothesized that transcriptomic (genome-wideexpression) profiling of the immune system via analysis of the wholeblood may be an effective way to stratify sepsis patients⁷. Importantinsights from these studies include overexpression of neutrophilsproteases, a collapse in adaptive immunity, and an overall profoundimmune dysregulation in sepsis⁷⁻¹². Some immune profiling techniqueshave been validated prospectively to show outcomes differences^(13,14),but no clinical tools have yet been translated into clinical practice.Still, most of these studies have been deposited in public databases forfurther re-analysis and re-use.

Thus, new molecular profiling tools are needed, both for improvedpatient care and resource stratification, but also as research tools forbetter clinical trials in sepsis.

SUMMARY

The invention relates to the use of biomarkers for aiding diagnosis,prognosis, and treatment of critically ill patients. In particular, theinvention relates to the use of biomarkers for prognosis of mortality incritically ill patients with sepsis, severe trauma, or burns.

Biomarkers that can be used in the practice of the invention includepolynucleotides comprising nucleotide sequences from genes or RNAtranscripts of genes, including but not limited to, DEFA4, CD163, PER1,RGS1, HIF1A, SEPP1, C11orf74, CIT, LY86, TST, OR52R1, and KCNJ2.

In certain embodiments, a panel of biomarkers is used for prognosis ofmortality. Biomarker panels of any size can be used in the practice ofthe invention. Biomarker panels for prognosis of mortality typicallycomprise at least 3 biomarkers and up to 30 biomarkers, including anynumber of biomarkers in between, such as 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,or 30 biomarkers. In certain embodiments, the invention includes abiomarker panel comprising at least 3, at least 4, or at least 5, or atleast 6, or at least 7, or at least 8, or at least 9, or at least 10, orat least 11 or more biomarkers. Although smaller biomarker panels areusually more economical, larger biomarker panels (i.e., greater than 30biomarkers) have the advantage of providing more detailed informationand can also be used in the practice of the invention.

In one embodiment, the biomarker panel comprises a plurality ofbiomarkers for prognosis of mortality, wherein the plurality ofbiomarkers comprises one or more polynucleotides comprising a nucleotidesequence from a gene or an RNA transcript of a gene selected from thegroup consisting of DEFA4, CD163, PER1, RGS1, HIF1A, SEPP1, C11orf74,CIT, LY86, TST, OR52R1, and KCNJ2. In one embodiment the biomarker panelcomprises a DEFA4 polynucleotide, a CD163 polynucleotide, a PER1polynucleotide, a RGS1 polynucleotide, an HIF1A polynucleotide, a SEPP1polynucleotide, a C11orf74 polynucleotide, a CIT polynucleotide, LY86polynucleotide, a TST polynucleotide, an OR52R1 polynucleotide, and aKCNJ2 polynucleotide.

In one aspect, the invention includes a method of determining mortalityrisk and treating a patient suspected of having a life-threateningcondition. The method comprises a) obtaining a biological sample fromthe patient; b) measuring levels of expression of DEFA4, CD163, PER1,RGS1, HIF1A, SEPP1, C11orf74, CIT, LY86, TST, OR52R1, and KCNJ2biomarkers in the biological sample; and c) analyzing the levels ofexpression of each biomarker in conjunction with respective referencevalue ranges for the biomarkers, wherein increased levels of expressionof the DEFA4, CD163, PER1, RGS1, HIF1A, SEPP1, C11orf74, and CITbiomarkers and decreased levels of expression of the LY86, TST, OR52R1,and KCNJ2 biomarkers compared to the reference value ranges for thebiomarkers for a control subject indicate that the patient is at highrisk of mortality within 30 days; and d) administering intensive careunit (ICU) treatment to the patient if the patient is at high risk ofmortality within 30 days. In certain embodiments, the life-threateningcondition is sepsis, trauma, or a burn.

The reference value ranges can represent the levels of one or morebiomarkers found in one or more samples of one or more subjects withouta critical illness (e.g., healthy subject or subject without infectionor injury). Alternatively, the reference values can represent the levelsof one or more biomarkers found in one or more samples of one or moresubjects with a critical illness (e.g., sepsis/infection, severe traumaor burn). In certain embodiments, the levels of the biomarkers arecompared to time-matched reference values ranges for non-infected andinfected/septic subjects or injured (e.g., severe trauma or burn) andnon-injured subjects.

In certain embodiments, the method is performed prior to or uponadmission to an intensive care unit. The method may be performed within14 days of admission to a hospital, such as within 24 hours, 48 hours, 3days, 4, days, 5, days, 6 days, 7 days, 8 days, 9 days, 10 days, 11days, 12 days, 13 days, or 14 days. In another embodiment, the method isperformed 3 to 7 days after diagnosis of sepsis in the patient. Inanother embodiment, the method is performed 3 to 14 days after burn ofthe patient. In a further embodiment, the method is performed 3 to 14days after injury of the patient.

The biological sample may comprise, for example, blood, buffy coat, bandcells, or metamyelocytes.

Biomarker polynucleotides (e.g., coding transcripts) can be detected,for example, by microarray analysis, polymerase chain reaction (PCR),reverse transcriptase polymerase chain reaction (RT-PCR), Northern blot,or serial analysis of gene expression (SAGE).

In another aspect, the invention includes a method of determining amortality gene score for a subject suspected of having alife-threatening condition, the method comprising: a) collecting abiological sample from the subject; b) measuring the levels of aplurality of biomarkers, described herein, in the biological sample; andc) determining the mortality gene score for the biomarkers bysubtracting the geometric mean of the expression levels of allbiomarkers that are underexpressed compared to control reference valuesfor the biomarkers from the geometric mean of the expression levels ofall biomarkers that are overexpressed compared to control referencevalues for the biomarkers, and multiplying the difference by the ratioof the number of biomarkers that are overexpressed to the number ofbiomarkers that are underexpressed compared to control reference valuesfor the biomarkers.

In another embodiment, the method further comprises calculating amortality gene score for the patient based on the levels of thebiomarkers, wherein a higher mortality gene score for the patientcompared to a control subject indicates that the patient is at high riskof mortality within 30 days.

In certain embodiments, the mortality gene score is calculated from theexpression levels of a plurality of biomarkers comprising one or morepolynucleotides comprising a nucleotide sequence from a gene or an RNAtranscript of a gene selected from the group consisting of DEFA4, CD163,PER1, RGS1, HIF1A, SEPP1, C11orf74, CIT, LY86, TST, OR52R1, and KCNJ2.In one embodiment, the plurality of biomarkers comprises a DEFA4polynucleotide, a CD163 polynucleotide, a PER1 polynucleotide, a RGS1polynucleotide, an HIF1A polynucleotide, a SEPP1 polynucleotide, aC11orf74 polynucleotide, a CIT polynucleotide, LY86 polynucleotide, aTST polynucleotide, an OR52R1 polynucleotide, and a KCNJ2polynucleotide.

In another embodiment, the invention includes a method of determiningmortality risk and treating a patient having sepsis, the methodcomprising: a) obtaining a biological sample from the patient; b)measuring levels of expression of DEFA4, CD163, PER1, RGS1, HIF1A,SEPP1, C11orf74, CIT, LY86, TST, OR52R1, and KCNJ2 biomarkers in thebiological sample; and c) calculating a mortality gene score for thepatient based on the levels of the biomarkers, wherein a highermortality gene score for the patient compared to a control subjectindicates that the patient is at high risk of mortality within 30 days;and d) administering intensive care unit treatment to the patient if thepatient is at high risk of mortality within 30 days.

In other embodiments, the invention includes a method of diagnosing andtreating a patient having an infection, the method comprising: a)obtaining a biological sample from the patient; b) measuring levels ofexpression of DEFA4, CD163, PER1, RGS1, HIF1A, SEPP1, C11orf74, CIT,LY86, TST, OR52R1, and KCNJ2 biomarkers in the biological sample,wherein increased levels of expression of the DEFA4, CD163, PER1, RGS1,HIF1A, SEPP1, C11orf74, and CIT biomarkers and decreased levels ofexpression of the LY86, TST, OR52R1, and KCNJ2 biomarkers compared tothe reference value ranges for the biomarkers for a control subjectindicate that the patient has sepsis; and c) administering a sepsistreatment comprising antimicrobial therapy, supportive care, or animmune-modulating therapy if the patient is diagnosed with sepsis. Inanother embodiment, the method further comprises calculating a mortalitygene score for the patient based on the levels of the biomarkers,wherein a higher mortality gene score for the patient compared to acontrol subject indicates that the patient is at high risk of mortalitywithin 30 days; and administering intensive care unit treatment to thepatient if the patient is at high risk of mortality within 30 days.

In another aspect, the invention includes a method of treating a patientsuspected of having a life-threatening condition, the method comprising:a) receiving information regarding the prognosis of the patientaccording to a method described herein; and b) administering intensivecare unit treatment to the patient if the patient is at high risk ofmortality within 30 days.

In certain embodiments, patient data is analyzed by one or more methodsincluding, but not limited to, multivariate linear discriminant analysis(LDA), receiver operating characteristic (ROC) analysis, principalcomponent analysis (PCA), ensemble data mining methods, cell specificsignificance analysis of microarrays (csSAM), and multi-dimensionalprotein identification technology (MUDPIT) analysis.

In another aspect, the invention includes a kit for prognosis ofmortality in a subject. The kit may include a container for holding abiological sample isolated from a human subject suspected of having alife-threatening condition, at least one agent that specifically detectsa biomarker; and printed instructions for reacting the agent with thebiological sample or a portion of the biological sample to detect thepresence or amount of at least one biomarker in the biological sample.The agents may be packaged in separate containers. The kit may furthercomprise one or more control reference samples and reagents forperforming PCR or microarray analysis for detection of biomarkers asdescribed herein.

In certain embodiments, the kit includes agents for detectingpolynucleotides of a biomarker panel comprising a plurality ofbiomarkers for prognosis of mortality, wherein one or more biomarkersare selected from the group consisting of a DEFA4 polynucleotide, aCD163 polynucleotide, a PER1 polynucleotide, a RGS1 polynucleotide, anHIF1A polynucleotide, a SEPP1 polynucleotide, a C11orf74 polynucleotide,a CIT polynucleotide, LY86 polynucleotide, a TST polynucleotide, anOR52R1 polynucleotide, and a KCNJ2 polynucleotide. In one embodiment,the kit includes agents for detecting biomarkers of a biomarker panelcomprising a DEFA4 polynucleotide, a CD163 polynucleotide, a PER1polynucleotide, a RGS1 polynucleotide, an HIF1A polynucleotide, a SEPP1polynucleotide, a C11orf74 polynucleotide, a CIT polynucleotide, LY86polynucleotide, a TST polynucleotide, an OR52R1 polynucleotide, and aKCNJ2 polynucleotide.

In certain embodiments, the kit comprises a microarray for analysis of aplurality of biomarker polynucleotides. In one embodiment, the kitcomprises a microarray comprising an oligonucleotide that hybridizes toa DEFA4 polynucleotide, an oligonucleotide that hybridizes to a CD163polynucleotide, an oligonucleotide that hybridizes to a PER1polynucleotide, an oligonucleotide that hybridizes to a RGS1polynucleotide, an oligonucleotide that hybridizes to an HIF1Apolynucleotide, an oligonucleotide that hybridizes to a SEPP1polynucleotide, an oligonucleotide that hybridizes to a C11orf74polynucleotide, an oligonucleotide that hybridizes to a CITpolynucleotide, an oligonucleotide that hybridizes to a LY86polynucleotide, an oligonucleotide that hybridizes to a TSTpolynucleotide, an oligonucleotide that hybridizes to an OR52R1polynucleotide, and an oligonucleotide that hybridizes to a KCNJ2polynucleotide.

In another aspect, the invention includes a diagnostic system comprisinga storage component (i.e., memory) for storing data, wherein the storagecomponent has instructions for determining the diagnosis of the subjectstored therein; a computer processor for processing data, wherein thecomputer processor is coupled to the storage component and configured toexecute the instructions stored in the storage component in order toreceive patient data and analyze patient data according to an algorithm;and a display component for displaying information regarding thediagnosis of the patient. The storage component may include instructionsfor determining the mortality risk of the subject, as described herein(see Examples 1). Additionally, the storage component may furtherinclude instructions for calculating a mortality gene score.

In certain embodiments, the invention includes a computer implementedmethod for determining mortality risk of a patient suspected of having alife-threatening condition, the computer performing steps comprising: a)receiving inputted patient data comprising values for levels ofexpression of DEFA4, CD163, PER1, RGS1, HIF1A, SEPP1, C11orf74, CIT,LY86, TST, OR52R1, and KCNJ2 biomarkers in a biological sample from thepatient; b) analyzing the level of each biomarker and comparing withrespective reference value ranges for each biomarker; c) calculating amortality gene score for the patient based on the levels of thebiomarkers, wherein a higher mortality gene score for the patientcompared to a control subject indicates that the patient is at high riskof mortality within 30 days; and d) displaying information regarding themortality risk of the patient.

In certain embodiments, the inputted patient data comprises values forthe levels of at least 12 biomarkers in a biological sample from thepatient. For example, the inputted patient data may comprises values forthe levels of a DEFA4 polynucleotide, a CD163 polynucleotide, a PER1polynucleotide, a RGS1 polynucleotide, an HIF1A polynucleotide, a SEPP1polynucleotide, a C11orf74 polynucleotide, a CIT polynucleotide, LY86polynucleotide, a TST polynucleotide, an OR52R1 polynucleotide, and aKCNJ2 polynucleotide.

In another embodiment, the invention includes a composition comprising aplurality of in vitro complexes, wherein the plurality of in vitrocomplexes comprise labeled probes hybridized to nucleic acids comprisingbiomarker DEFA4, CD163, PER1, RGS1, HIF1A, SEPP1, C11orf74, CIT, LY86,TST, OR52R1, and KCNJ2 gene sequences, said labeled probes hybridized tothe biomarker gene sequences, or their complements, wherein said nucleicacids are extracted from a patient who has a life-threatening condition(e.g., sepsis, severe trauma, or burn) or are amplification products ofthe nucleic acids extracted from the patient who has a life-threateningcondition. Probes may be detectably labeled with any type of label,including, but not limited to, a fluorescent label, bioluminescentlabel, chemiluminescent label, colorimetric label, or isotopic label(e.g., stable trace isotope or radioactive isotope). In certainembodiments, the composition is in a detection device (i.e., devicecapable of detecting labeled probe).

These and other embodiments of the subject invention will readily occurto those of skill in the art in view of the disclosure herein.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows the overall study schematic.

FIG. 2 shows summary ROC curves from discovery datasets. Shown is theprognostic power of the 12-gene set for prediction of mortality inseptic patients at admission.

FIG. 3 shows Forest plots of the alpha and beta parameters for thesummary ROC curve in the discovery datasets from FIG. 2.

FIGS. 4-6 show violin plots of the 12-gene scores for individualpatients in the discovery cohorts, separate by survivor status. Innerbars show mean and inter-quartile range.

FIG. 7 shows summary ROC curves from validation datasets. Shown is theprognostic power of the 12-gene set for prediction of mortality inseptic patients at admission.

FIG. 8 shows Forest plots of the alpha and beta parameters for thesummary ROC curve in the validation datasets from FIG. 7.

FIG. 9 shows violin plots of the 12-gene scores for individual patientsin the validation cohorts, separate by survivor status. Inner bars showmean and inter-quartile range.

FIGS. 10A and 10B show longitudinal data from validation datasetGSE21802. FIG. 10A shows ROC plots of mortality prediction broken intobins of time since admission. FIG. 10B shows individual patienttrajectories, separated by survivor status.

FIGS. 11A and 11B show longitudinal data from validation datasetGSE54514. FIG. 11A shows ROC plots of mortality prediction broken intobins of time since admission. FIG. 11B shows individual patienttrajectories, separated by survivor status.

FIGS. 12A and 12B show longitudinal data from the Glue Granttrauma-buffy coat cohort. FIG. 12A shows individual patient trajectoriesof the 12-gene score, separated by survivor status. FIG. 12B showsindividual patient trajectories of the MODS score, separated by survivorstatus.

FIGS. 13A and 13B show ROC plots of mortality prediction broken intobins of time since admission in the Glue Grant trauma-buffy coat cohort.FIG. 13A shows sepsis patients in Day 6-Day 10 time window at time ofdiagnosis (N=12 survivors, 1 non-TBI death). FIG. 13B shows all patientsbroken into bins based on day since admission; shown is all-causemortality prediction. N=179 survivors and 8 non-survivors at Day 0;later timepoints show the same patients.

FIGS. 14A and 14B show longitudinal data from the Glue Grant burns-wholeblood cohort. FIG. 14A shows individual patient trajectories of the12-gene score, separated by survivor status. FIG. 14B shows individualpatient trajectories of the Denver score, separated by survivor status.

FIGS. 15A and 15B show ROC plots of mortality prediction broken intobins of time since admission in the Glue Grant burns-whole blood cohort.FIG. 15A shows sepsis patients at Day 1-Day 3 (N=63 survivors, 3non-survivors) and Day 3-Day 7 (N=15 survivors, 5 non-survivors) timewindows at time of diagnosis. FIG. 15B shows all patients broken intobins based on day since admission; shown is all-cause mortalityprediction. N=212 survivors and 22 non-survivors at Day 0; latertimepoints show the same patients.

FIGS. 16A and 16B show cell-type enrichment plots of the 122-gene set(FIG. 16A) and the 12-gene set (FIG. 16B). Shown are Z-scores of theenrichment of each gene set across all sorted in vitro cell types shown.

DETAILED DESCRIPTION

The practice of the present invention will employ, unless otherwiseindicated, conventional methods of pharmacology, chemistry,biochemistry, recombinant DNA techniques and immunology, within theskill of the art. Such techniques are explained fully in the literature.See, e.g., J. R. Brown Sepsis: Symptoms, Diagnosis and Treatment (PublicHealth in the 21st Century Series, Nova Science Publishers, Inc., 2013);Sepsis and Non-infectious Systemic Inflammation: From Biology toCritical Care (J. Cavaillon, C. Adrie eds., Wiley-Blackwell, 2008);Sepsis: Diagnosis, Management and Health Outcomes (Allergies andInfectious Diseases, N. Khardori ed., Nova Science Pub Inc., 2014);Handbook of Experimental Immunology, Vols. I-IV (D. M. Weir and C. C.Blackwell eds., Blackwell Scientific Publications); A. L. Lehninger,Biochemistry (Worth Publishers, Inc., current addition); Sambrook, etal., Molecular Cloning: A Laboratory Manual (3rd Edition, 2001); MethodsIn Enzymology (S. Colowick and N. Kaplan eds., Academic Press, Inc.).

All publications, patents and patent applications cited herein, whethersupra or infra, are hereby incorporated by reference in theirentireties.

I. DEFINITIONS

In describing the present invention, the following terms will beemployed, and are intended to be defined as indicated below.

It must be noted that, as used in this specification and the appendedclaims, the singular forms “a,” “an,” and “the” include plural referentsunless the content clearly dictates otherwise. Thus, for example,reference to “a biomarker” includes a mixture of two or more biomarkers,and the like.

The term “about,” particularly in reference to a given quantity, ismeant to encompass deviations of plus or minus five percent.

The term “survival” means the time from a determination of mortalityrisk of a subject, using biomarkers as described herein, to the time ofdeath.

The term “survivor” as used herein refers to a subject who will live forat least 30 more days.

The term “non-survivor” as used herein refers to a subject who will diewithin 30 days.

A “biomarker” in the context of the present invention refers to abiological compound, such as a polynucleotide which is differentiallyexpressed in a sample taken from a survivor as compared to a comparablesample taken from a non-survivor of a critical illness or condition(e.g., sepsis, severe trauma, or burn). The biomarker can be a nucleicacid, a fragment of a nucleic acid, a polynucleotide, or anoligonucleotide that can be detected and/or quantified. Biomarkersinclude polynucleotides comprising nucleotide sequences from genes orRNA transcripts of genes, including but not limited to, DEFA4, CD163,PER1, RGS1, HIF1A, SEPP1, C11orf74, CIT, LY86, TST, OR52R1, and KCNJ2.

The terms “polypeptide” and “protein” refer to a polymer of amino acidresidues and are not limited to a minimum length. Thus, peptides,oligopeptides, dimers, multimers, and the like, are included within thedefinition. Both full-length proteins and fragments thereof areencompassed by the definition. The terms also include postexpressionmodifications of the polypeptide, for example, glycosylation,acetylation, phosphorylation, hydroxylation, oxidation, and the like.

The terms “polynucleotide,” “oligonucleotide,” “nucleic acid” and“nucleic acid molecule” are used herein to include a polymeric form ofnucleotides of any length, either ribonucleotides ordeoxyribonucleotides. This term refers only to the primary structure ofthe molecule. Thus, the term includes triple-, double- andsingle-stranded DNA, as well as triple-, double- and single-strandedRNA. It also includes modifications, such as by methylation and/or bycapping, and unmodified forms of the polynucleotide. More particularly,the terms “polynucleotide,” “oligonucleotide,” “nucleic acid” and“nucleic acid molecule” include polydeoxyribonucleotides (containing2-deoxy-D-ribose), polyribonucleotides (containing D-ribose), and anyother type of polynucleotide which is an N- or C-glycoside of a purineor pyrimidine base. There is no intended distinction in length betweenthe terms “polynucleotide,” “oligonucleotide,” “nucleic acid” and“nucleic acid molecule,” and these terms are used interchangeably.

The phrase “differentially expressed” refers to differences in thequantity and/or the frequency of a biomarker present in a sample takenfrom patients having a life-threatening condition (e.g., sepsis, severetrauma, or burn) at high risk of mortality within 30 days (i.e.,non-survivor) as compared to a control subject at low risk of mortalitywithin 30 days (i.e., survivor). For example, a biomarker can be apolynucleotide which is present at an elevated level or at a decreasedlevel in samples of patients with sepsis compared to samples of controlsubjects. Alternatively, a biomarker can be a polynucleotide which isdetected at a higher frequency or at a lower frequency in samples ofpatients at high risk of mortality within 30 days (i.e., non-survivors)compared to samples of control subjects. A biomarker can bedifferentially present in terms of quantity, frequency or both.

A polynucleotide is differentially expressed between two samples if theamount of the polynucleotide in one sample is statisticallysignificantly different from the amount of the polynucleotide in theother sample. For example, a polynucleotide is differentially expressedin two samples if it is present at least about 120%, at least about130%, at least about 150%, at least about 180%, at least about 200%, atleast about 300%, at least about 500%, at least about 700%, at leastabout 900%, or at least about 1000% greater than it is present in theother sample, or if it is detectable in one sample and not detectable inthe other.

Alternatively or additionally, a polynucleotide is differentiallyexpressed in two sets of samples if the frequency of detecting thepolynucleotide in samples of patients at high risk of mortality within30 days (i.e., non-survivors) is statistically significantly higher orlower than in the control samples. For example, a polynucleotide isdifferentially expressed in two sets of samples if it is detected atleast about 120%, at least about 130%, at least about 150%, at leastabout 180%, at least about 200%, at least about 300%, at least about500%, at least about 700%, at least about 900%, or at least about 1000%more frequently or less frequently observed in one set of samples thanthe other set of samples.

A “similarity value” is a number that represents the degree ofsimilarity between two things being compared. For example, a similarityvalue may be a number that indicates the overall similarity between apatient's expression profile using specific phenotype-related biomarkersand reference value ranges for the biomarkers in one or more controlsamples or a reference expression profile (e.g., the similarity to a“survivor” expression profile or a “non-survivor” expression profile).The similarity value may be expressed as a similarity metric, such as acorrelation coefficient, or may simply be expressed as the expressionlevel difference, or the aggregate of the expression level differences,between levels of biomarkers in a patient sample and a control sample orreference expression profile.

The terms “subject,” “individual,” and “patient,” are usedinterchangeably herein and refer to any mammalian subject for whomdiagnosis, prognosis, treatment, or therapy is desired, particularlyhumans. Other subjects may include cattle, dogs, cats, guinea pigs,rabbits, rats, mice, horses, and so on. In some cases, the methods ofthe invention find use in experimental animals, in veterinaryapplication, and in the development of animal models for disease,including, but not limited to, rodents including mice, rats, andhamsters; and primates.

As used herein, a “biological sample” refers to a sample of tissue,cells, or fluid isolated from a subject, including but not limited to,for example, blood, buffy coat, plasma, serum, blood cells (e.g.,peripheral blood mononucleated cells (PBMCS), band cells, neutrophils,metamyelocytes, monocytes, or T cells), fecal matter, urine, bonemarrow, bile, spinal fluid, lymph fluid, samples of the skin, externalsecretions of the skin, respiratory, intestinal, and genitourinarytracts, tears, saliva, milk, organs, biopsies and also samples of invitro cell culture constituents, including, but not limited to,conditioned media resulting from the growth of cells and tissues inculture medium, e.g., recombinant cells, and cell components.

A “test amount” of a biomarker refers to an amount of a biomarkerpresent in a sample being tested. A test amount can be either anabsolute amount (e.g., μg/ml) or a relative amount (e.g., relativeintensity of signals).

A “diagnostic amount” of a biomarker refers to an amount of a biomarkerin a subject's sample that is consistent with a diagnosis of sepsis orprognosis of mortality. A diagnostic amount can be either an absoluteamount (e.g., μg/ml) or a relative amount (e.g., relative intensity ofsignals).

A “control amount” of a biomarker can be any amount or a range of amountwhich is to be compared against a test amount of a biomarker. Forexample, a control amount of a biomarker can be the amount of abiomarker in a person without a life-threatening condition (e.g., personwithout sepsis, severe trauma, or burn), healthy person, or a survivor.A control amount can be either in absolute amount (e.g., μg/ml) or arelative amount (e.g., relative intensity of signals).

The term “antibody” encompasses polyclonal and monoclonal antibodypreparations, as well as preparations including hybrid antibodies,altered antibodies, chimeric antibodies and, humanized antibodies, aswell as: hybrid (chimeric) antibody molecules (see, for example, Winteret al. (1991) Nature 349:293-299; and U.S. Pat. No. 4,816,567); F(ab′)₂and F(ab) fragments; F_(v) molecules (noncovalent heterodimers, see, forexample, Inbar et al. (1972) Proc Natl Acad Sci USA 69:2659-2662; andEhrlich et al. (1980) Biochem 19:4091-4096); single-chain F_(v)molecules (sFv) (see, e.g., Huston et al. (1988) Proc Natl Acad Sci USA85:5879-5883); dimeric and trimeric antibody fragment constructs;minibodies (see, e.g., Pack et al. (1992) Biochem 31:1579-1584; Cumberet al. (1992) J Immunology 149B:120-126); humanized antibody molecules(see, e.g., Riechmann et al. (1988) Nature 332:323-327; Verhoeyan et al.(1988) Science 239:1534-1536; and U.K. Patent Publication No. GB2,276,169, published 21 Sep. 1994); and, any functional fragmentsobtained from such molecules, wherein such fragments retainspecific-binding properties of the parent antibody molecule.

“Detectable moieties” or “detectable labels” contemplated for use in theinvention include, but are not limited to, radioisotopes, fluorescentdyes such as fluorescein, phycoerythrin, Cy-3, Cy-5, allophycoyanin,DAPI, Texas Red, rhodamine, Oregon green, Lucifer yellow, and the like,green fluorescent protein (GFP), red fluorescent protein (DsRed), CyanFluorescent Protein (CFP), Yellow Fluorescent Protein (YFP), CerianthusOrange Fluorescent Protein (cOFP), alkaline phosphatase (AP),beta-lactamase, chloramphenicol acetyltransferase (CAT), adenosinedeaminase (ADA), aminoglycoside phosphotransferase (neo^(r), G418^(r))dihydrofolate reductase (DHFR), hygromycin-B-phosphotransferase (HPH),thymidine kinase (TK), lacZ (encoding β-galactosidase), and xanthineguanine phosphoribosyltransferase (XGPRT), Beta-Glucuronidase (gus),Placental Alkaline Phosphatase (PLAP), Secreted Embryonic AlkalinePhosphatase (SEAP), or Firefly or Bacterial Luciferase (LUC). Enzymetags are used with their cognate substrate. The terms also includecolor-coded microspheres of known fluorescent light intensities (seee.g., microspheres with xMAP technology produced by Luminex (Austin,Tex.); microspheres containing quantum dot nanocrystals, for example,containing different ratios and combinations of quantum dot colors(e.g., Qdot nanocrystals produced by Life Technologies (Carlsbad,Calif.); glass coated metal nanoparticles (see e.g., SERS nanotagsproduced by Nanoplex Technologies, Inc. (Mountain View, Calif.); barcodematerials (see e.g., sub-micron sized striped metallic rods such asNanobarcodes produced by Nanoplex Technologies, Inc.), encodedmicroparticles with colored bar codes (see e.g., CellCard produced byVitra Bioscience, vitrabio.com), and glass microparticles with digitalholographic code images (see e.g., CyVera microbeads produced byIllumina (San Diego, Calif.). As with many of the standard proceduresassociated with the practice of the invention, skilled artisans will beaware of additional labels that can be used.

“Diagnosis” as used herein generally includes determination as towhether a subject is likely affected by a given disease, disorder ordysfunction. The skilled artisan often makes a diagnosis on the basis ofone or more diagnostic indicators, i.e., a biomarker, the presence,absence, or amount of which is indicative of the presence or absence ofthe disease, disorder or dysfunction.

“Prognosis” as used herein generally refers to a prediction of theprobable course and outcome of a clinical condition or disease. Aprognosis of a patient is usually made by evaluating factors or symptomsof a disease that are indicative of a favorable or unfavorable course oroutcome of the disease. It is understood that the term “prognosis” doesnot necessarily refer to the ability to predict the course or outcome ofa condition with 100% accuracy. Instead, the skilled artisan willunderstand that the term “prognosis” refers to an increased probabilitythat a certain course or outcome will occur; that is, that a course oroutcome is more likely to occur in a patient exhibiting a givencondition, when compared to those individuals not exhibiting thecondition.

“Substantially purified” refers to nucleic acid molecules or proteinsthat are removed from their natural environment and are isolated orseparated, and are at least about 60% free, preferably about 75% free,and most preferably about 90% free, from other components with whichthey are naturally associated.

II. MODES OF CARRYING OUT THE INVENTION

Before describing the present invention in detail, it is to beunderstood that this invention is not limited to particular formulationsor process parameters as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments of the invention only, and is notintended to be limiting.

Although a number of methods and materials similar or equivalent tothose described herein can be used in the practice of the presentinvention, the preferred materials and methods are described herein.

The invention relates to the use of biomarkers either alone or incombination with clinical parameters for aiding diagnosis, prognosis,and treatment of critically ill patients. In particular, the inventorshave discovered biomarkers whose expression profiles can be used forprognosis of mortality in critically ill patients with sepsis, severetrauma, or burns (see Example 1).

In order to further an understanding of the invention, a more detaileddiscussion is provided below regarding the identified biomarkers andmethods of using them in diagnosis, prognosis, and treatment ofcritically ill patients.

A. Biomarkers Biomarkers that can be used in the practice of theinvention include polynucleotides comprising nucleotide sequences fromgenes or RNA transcripts of genes, including but not limited to, DEFA4,CD163, PER1, RGS1, HIF1A, SEPP1, C11orf74, CIT, LY86, TST, OR52R1, andKCNJ2. Differential expression of these biomarkers is associated with ahigh risk of mortality (within 30 days) and therefore expressionprofiles of these biomarkers are useful for prognosis of mortality incritically ill patients.

Accordingly, in one aspect, the invention provides a method ofdetermining mortality risk of a subject, comprising measuring the levelof a plurality of biomarkers in a biological sample derived from asubject suspected of having a life-threatening condition, and analyzingthe levels of the biomarkers and comparing with respective referencevalue ranges for the biomarkers, wherein differential expression of oneor more biomarkers in the biological sample compared to one or morebiomarkers in a control sample indicates that the subject is at highrisk of mortality within 30 days.

When analyzing the levels of biomarkers in a biological sample, thereference value ranges can represent the levels of one or morebiomarkers found in one or more samples of one or more subjects withouta critical illness (e.g., a survivor, healthy subject, or subjectwithout infection or injury). Alternatively, the reference values canrepresent the levels of one or more biomarkers found in one or moresamples of one or more subjects with a critical illness (e.g., anon-survivor, a subject with sepsis/infection, severe trauma, or burn).In certain embodiments, the levels of the biomarkers are compared totime-matched reference values ranges for non-infected andinfected/septic subjects or injured (e.g., severe trauma or burn) andnon-injured subjects.

The biological sample obtained from the subject to be diagnosed istypically whole blood, buffy coat, plasma, serum, or blood cells (e.g.,peripheral blood mononucleated cells (PBMCS), band cells,metamyelocytes, neutrophils, monocytes, or T cells), but can be anysample from bodily fluids, tissue or cells that contain the expressedbiomarkers. A “control” sample, as used herein, refers to a biologicalsample, such as a bodily fluid, tissue, or cells that are not diseased.That is, a control sample is obtained from a normal subject (e.g. anindividual known to not have a life-threatening condition), a person whodoes not have sepsis, severe trauma, or burn, or a survivor. Abiological sample can be obtained from a subject by conventionaltechniques. For example, blood can be obtained by venipuncture, andsolid tissue samples can be obtained by surgical techniques according tomethods well known in the art.

In certain embodiments, a panel of biomarkers is used for prognosis ofmortality risk. Biomarker panels of any size can be used in the practiceof the invention. Biomarker panels for prognosis of mortality typicallycomprise at least 3 biomarkers and up to 30 biomarkers, including anynumber of biomarkers in between, such as 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,or 30 biomarkers. In certain embodiments, the invention includes abiomarker panel comprising at least 3, or at least 4, or at least 5, orat least 6, or at least 7, or at least 8, or at least 9, or at least 10,or at least 11 or more biomarkers. Although smaller biomarker panels areusually more economical, larger biomarker panels (i.e., greater than 30biomarkers) have the advantage of providing more detailed informationand can also be used in the practice of the invention.

In certain embodiments, the invention includes a panel of biomarkers forprognosis of mortality risk comprising one or more polynucleotidescomprising a nucleotide sequence from a gene or an RNA transcript of agene selected from the group consisting of DEFA4, CD163, PER1, RGS1,HIF1A, SEPP1, C11orf74, CIT, LY86, TST, OR52R1, and KCNJ2. In oneembodiment, the panel of biomarkers comprises a DEFA4 polynucleotide, aCD163 polynucleotide, a PER1 polynucleotide, an RGS1 polynucleotide, anHIF1A polynucleotide, a SEPP1 polynucleotide, a C11orf74 polynucleotide,a CIT polynucleotide, LY86 polynucleotide, a TST polynucleotide, anOR52R1 polynucleotide, and a KCNJ2 polynucleotide.

In certain embodiments, a mortality gene score is used for prognosis ofmortality risk. The mortality gene score is calculated by subtractingthe geometric mean of the expression levels of all measured biomarkersthat are underexpressed compared to control reference values for thebiomarkers from the geometric mean of the expression levels of allmeasured biomarkers that are overexpressed compared to control referencevalues for the biomarkers, and multiplying the difference by the ratioof the number of biomarkers that are overexpressed to the number ofbiomarkers that are underexpressed compared to control reference valuesfor the biomarkers. A higher mortality gene score for the subjectcompared to reference value ranges for control subjects indicates thatthe subject has a high risk of mortality within 30 days (see Example 1).

The methods described herein may be used to identify patients at highrisk of mortality who should receive immediate intensive care. Forexample, patients identified as having a high risk of mortality within30 days by the methods described herein can be sent immediately to theICU for treatment, whereas patients identified as having a low risk ofmortality within 30 days may be further monitored and/or treated in aregular hospital ward. Both patients and clinicians can benefit frombetter estimates of mortality risk, which allows timely discussions ofpatients' preferences and their choices regarding life-saving measures.Better molecular phenotyping of patients also makes possibleimprovements in clinical trials, both in 1) patient selection for drugsand interventions and 2) assessment of observed-to-expected ratios ofsubject mortality.

ICU treatment of a patient, identified as having a high risk ofmortality within 30 days, may comprise constant monitoring of bodilyfunctions and providing life support equipment and/or medications torestore normal bodily function. ICU treatment may include, for example,using mechanical ventilators to assist breathing, equipment formonitoring bodily functions (e.g., heart and pulse rate, air flow to thelungs, blood pressure and blood flow, central venous pressure, amount ofoxygen in the blood, and body temperature), pacemakers, defibrillators,dialysis equipment, intravenous lines, feeding tubes, suction pumps,drains, and/or catheters, and/or administering various drugs fortreating the life threatening condition (e.g., sepsis, severe trauma, orburn). ICU treatment may further comprise administration of one or moreanalgesics to reduce pain, and/or sedatives to induce sleep or relieveanxiety, and/or barbiturates (e.g., pentobarbital or thiopental) tomedically induce coma.

In certain embodiments, a critically ill patient diagnosed with a viralinfection is further administered a therapeutically effective dose of anantiviral agent, such as a broad-spectrum antiviral agent, an antiviralvaccine, a neuraminidase inhibitor (e.g., zanamivir (Relenza) andoseltamivir (Tamiflu)), a nucleoside analogue (e.g., acyclovir,zidovudine (AZT), and lamivudine), an antisense antiviral agent (e.g.,phosphorothioate antisense antiviral agents (e.g., Fomivirsen(Vitravene) for cytomegalovirus retinitis), morpholino antisenseantiviral agents), an inhibitor of viral uncoating (e.g., Amantadine andrimantadine for influenza, Pleconaril for rhinoviruses), an inhibitor ofviral entry (e.g., Fuzeon for HIV), an inhibitor of viral assembly(e.g., Rifampicin), or an antiviral agent that stimulates the immunesystem (e.g., interferons). Exemplary antiviral agents include Abacavir,Aciclovir, Acyclovir, Adefovir, Amantadine, Amprenavir, Ampligen,Arbidol, Atazanavir, Atripla (fixed dose drug), Balavir, Cidofovir,Combivir (fixed dose drug), Dolutegravir, Darunavir, Delavirdine,Didanosine, Docosanol, Edoxudine, Efavirenz, Emtricitabine, Enfuvirtide,Entecavir, Ecoliever, Famciclovir, Fixed dose combination(antiretroviral), Fomivirsen, Fosamprenavir, Foscarnet, Fosfonet, Fusioninhibitor, Ganciclovir, Ibacitabine, Imunovir, Idoxuridine, Imiquimod,Indinavir, Inosine, Integrase inhibitor, Interferon type III, Interferontype II, Interferon type I, Interferon, Lamivudine, Lopinavir, Loviride,Maraviroc, Moroxydine, Methisazone, Nelfinavir, Nevirapine, Nexavir,Nitazoxanide, Nucleoside analogues, Novir, Oseltamivir (Tamiflu),Peginterferon alfa-2a, Penciclovir, Peramivir, Pleconaril,Podophyllotoxin, Protease inhibitor, Raltegravir, Reverse transcriptaseinhibitor, Ribavirin, Rimantadine, Ritonavir, Pyramidine, Saquinavir,Sofosbuvir, Stavudine, Synergistic enhancer (antiretroviral),Telaprevir, Tenofovir, Tenofovir disoproxil, Tipranavir, Trifluridine,Trizivir, Tromantadine, Truvada, Valaciclovir (Valtrex), Valganciclovir,Vicriviroc, Vidarabine, Viramidine, Zalcitabine, Zanamivir (Relenza),and Zidovudine.

In certain embodiments, a critically ill patient diagnosed with abacterial infection is further administered a therapeutically effectivedose of an antibiotic. Antibiotics may include broad spectrum,bactericidal, or bacteriostatic antibiotics. Exemplary antibioticsinclude aminoglycosides such as Amikacin, Amikin, Gentamicin, Garamycin,Kanamycin, Kantrex, Neomycin, Neo-Fradin, Netilmicin, Netromycin,Tobramycin, Nebcin, Paromomycin, Humatin, Streptomycin,Spectinomycin(Bs), and Trobicin; ansamycins such as Geldanamycin,Herbimycin, Rifaximin, and Xifaxan; carbacephems such as Loracarbef andLorabid; carbapenems such as Ertapenem, Invanz, Doripenem, Doribax,Imipenem/Cilastatin, Primaxin, Meropenem, and Merrem; cephalosporinssuch as Cefadroxil, Duricef, Cefazolin, Ancef, Cefalotin or Cefalothin,Keflin, Cefalexin, Keflex, Cefaclor, Distaclor, Cefamandole, Mandol,Cefoxitin, Mefoxin, Cefprozil, Cefzil, Cefuroxime, Ceftin, Zinnat,Cefixime, Cefdinir, Cefditoren, Cefoperazone, Cefotaxime, Cefpodoxime,Ceftazidime, Ceftibuten, Ceftizoxime, Ceftriaxone, Cefepime, Maxipime,Ceftaroline fosamil, Teflaro, Ceftobiprole, and Zeftera; glycopeptidessuch as Teicoplanin, Targocid, Vancomycin, Vancocin, Telavancin,Vibativ, Dalbavancin, Dalvance, Oritavancin, and Orbactiv; lincosamidessuch as Clindamycin, Cleocin, Lincomycin, and Lincocin; lipopeptidessuch as Daptomycin and Cubicin; macrolides such as Azithromycin,Zithromax, Surnamed, Xithrone, Clarithromycin, Biaxin, Dirithromycin,Dynabac, Erythromycin, Erythocin, Erythroped, Roxithromycin,Troleandomycin, Tao, Telithromycin, Ketek, Spiramycin, and Rovamycine;monobactams such as Aztreonam and Azactam; nitrofurans such asFurazolidone, Furoxone, Nitrofurantoin, Macrodantin, and Macrobid;oxazolidinones such as Linezolid, Zyvox, VRSA, Posizolid, Radezolid, andTorezolid; penicillins such as Penicillin V, Veetids (Pen-Vee-K),Piperacillin, Pipracil, Penicillin G, Pfizerpen, Temocillin, Negaban,Ticarcillin, and Ticar; penicillin combinations such asAmoxicillin/clavulanate, Augmentin, Ampicillin/sulbactam, Unasyn,Piperacillin/tazobactam, Zosyn, Ticarcillin/clavulanate, and Timentin;polypeptides such as Bacitracin, Colistin, Coly-Mycin-S, and PolymyxinB; quinolones/fluoroquinolones such as Ciprofloxacin, Cipro, Ciproxin,Ciprobay, Enoxacin, Penetrex, Gatifloxacin, Tequin, Gemifloxacin,Factive, Levofloxacin, Levaquin, Lomefloxacin, Maxaquin, Moxifloxacin,Avelox, Nalidixic acid, NegGram, Norfloxacin, Noroxin, Ofloxacin,Floxin, Ocuflox Trovafloxacin, Trovan, Grepafloxacin, Raxar,Sparfloxacin, Zagam, Temafloxacin, and Omniflox; sulfonamides such asAmoxicillin, Novamox, Amoxil, Ampicillin, Principen, Azlocillin,Carbenicillin, Geocillin, Cloxacillin, Tegopen, Dicloxacillin, Dynapen,Flucloxacillin, Floxapen, Mezlocillin, Mezlin, Methicillin, Staphcillin,Nafcillin, Unipen, Oxacillin, Prostaphlin, Penicillin G, Pentids,Mafenide, Sulfamylon, Sulfacetamide, Sulamyd, Bleph-10, Sulfadiazine,Micro-Sulfon, Silver sulfadiazine, Silvadene, SulfadimethoxineDi-Methox, Albon, Sulfamethizole, Thiosulfil Forte, Sulfamethoxazole,Gantanol, Sulfanilimide, Sulfasalazine, Azulfidine, Sulfisoxazole,Gantrisin, Trimethoprim-Sulfamethoxazole (Co-trimoxazole) (TMP-SMX),Bactrim, Septra, Sulfonamidochrysoidine, and Prontosil; tetracyclinessuch as Demeclocycline, Declomycin, Doxycycline, Vibramycin,Minocycline, Minocin, Oxytetracycline, Terramycin, Tetracycline andSumycin, Achromycin V, and Steclin; drugs against mycobacteria such asClofazimine, Lamprene, Dapsone, Avlosulfon, Capreomycin, Capastat,Cycloserine, Seromycin, Ethambutol, Myambutol, Ethionamide, Trecator,Isoniazid, I.N.H., Pyrazinamide, Aldinamide, Rifampicin, Rifadin,Rimactane, Rifabutin, Mycobutin, Rifapentine, Priftin, and Streptomycin;others antibiotics such as Arsphenamine, Salvarsan, Chloramphenicol,Chloromycetin, Fosfomycin, Monurol, Monuril, Fusidic acid, Fucidin,Metronidazole, Flagyl, Mupirocin, Bactroban, Platensimycin,Quinupristin/Dalfopristin, Synercid, Thiamphenicol, Tigecycline,Tigacyl, Tinidazole, Tindamax Fasigyn, Trimethoprim, Proloprim, andTrimpex.

In another embodiment, the invention includes a method of diagnosing andtreating a patient having an infection, the method comprising: a)obtaining a biological sample from the patient; b) measuring levels ofexpression of DEFA4, CD163, PER1, RGS1, HIF1A, SEPP1, C11orf74, CIT,LY86, TST, OR52R1, and KCNJ2 biomarkers in the biological sample,wherein increased levels of expression of the DEFA4, CD163, PER1, RGS1,HIF1A, SEPP1, C11orf74, and CIT biomarkers and decreased levels ofexpression of the LY86, TST, OR52R1, and KCNJ2 biomarkers compared tothe reference value ranges for the biomarkers for a control subjectindicate that the patient has sepsis; and c) administering a sepsistreatment if the patient is diagnosed with sepsis. In some embodiments,the method further comprises calculating a mortality gene score for thepatient based on the levels of the biomarkers, wherein a highermortality gene score for the patient compared to a control subjectindicates that the patient is at high risk of mortality within 30 days;and administering intensive care unit treatment to the patient if thepatient is at high risk of mortality within 30 days.

Sepsis treatment may comprise, for example, administering antimicrobialtherapy, supportive care, or an immune-modulating therapy, or acombination thereof. Antimicrobial therapy may include administration ofone or more drugs against all pathogens the patient is likely to beinfected with (e.g., bacterial and/or fungal and/or viral) withpreferably broad-spectrum coverage using combinations of antimicrobialagents. Combination antimicrobial therapy may include at least twodifferent classes of antibiotics (e.g., a beta-lactam agent with amacrolide, fluoroquinolone, or aminoglycoside). Broad spectrumantibiotics may be administered in combination with antifungal and/orantiviral agents. Supportive therapy for sepsis may includeadministration of oxygen, blood transfusions, mechanical ventilation,fluid therapy (e.g., fluid administration with crystalloids and/oralbumin continued until the patient shows hemodynamic improvement),nutrition (e.g., oral or enteral feedings), blood glucose management,vasopressor therapy (e.g. administration of norepinephrine, epinephrine,and/or vasopressin to maintain adequate blood pressure), inotropictherapy (e.g., dobutamine), renal replacement therapy (e.g., dialysis),bicarbonate therapy, pharmacoprophylaxis against venous thromboembolism(e.g., treatment with heparin or intermittent pneumatic compressiondevice), stress ulcer prophylaxis, sedation, analgesia, neuromuscularblockade, insulin (e.g., to maintain stable blood sugar levels), orcorticosteroids (e.g., hydrocortisone), or any combination thereof.Immune-modulating therapy may include administration of activatedprotein C, immunoglobulin therapy, anti-platelet therapy,cytokine-blocking therapy, dialysis for pathogenic proteins or withantibiotic cartridges, or any combination thereof.

B. Detecting and Measuring Biomarkers

It is understood that the biomarkers in a sample can be measured by anysuitable method known in the art. Measurement of the expression level ofa biomarker can be direct or indirect. For example, the abundance levelsof RNAs or proteins can be directly quantitated. Alternatively, theamount of a biomarker can be determined indirectly by measuringabundance levels of cDNAs, amplified RNAs or DNAs, or by measuringquantities or activities of RNAs, proteins, or other molecules (e.g.,metabolites) that are indicative of the expression level of thebiomarker. The methods for measuring biomarkers in a sample have manyapplications. For example, one or more biomarkers can be measured to aidin the prognosis of mortality risk, to determine the appropriatetreatment for a subject, to monitor responses in a subject to treatment,or to identify therapeutic compounds that modulate expression of thebiomarkers in vivo or in vitro.

Detecting Biomarker Polynucleotides

In one embodiment, the expression levels of the biomarkers aredetermined by measuring polynucleotide levels of the biomarkers. Thelevels of transcripts of specific biomarker genes can be determined fromthe amount of mRNA, or polynucleotides derived therefrom, present in abiological sample. Polynucleotides can be detected and quantitated by avariety of methods including, but not limited to, microarray analysis,polymerase chain reaction (PCR), reverse transcriptase polymerase chainreaction (RT-PCR), Northern blot, and serial analysis of gene expression(SAGE). See, e.g., Draghici Data Analysis Tools for DNA Microarrays,Chapman and Hall/CRC, 2003; Simon et al. Design and Analysis of DNAMicroarray Investigations, Springer, 2004; Real-Time PCR: CurrentTechnology and Applications, Logan, Edwards, and Saunders eds., CaisterAcademic Press, 2009; Bustin A-Z of Quantitative PCR (IUL Biotechnology,No. 5), International University Line, 2004; Velculescu et al. (1995)Science 270: 484-487; Matsumura et al. (2005) Cell. Microbiol. 7: 11-18;Serial Analysis of Gene Expression (SAGE): Methods and Protocols(Methods in Molecular Biology), Humana Press, 2008; herein incorporatedby reference in their entireties.

In one embodiment, microarrays are used to measure the levels ofbiomarkers. An advantage of microarray analysis is that the expressionof each of the biomarkers can be measured simultaneously, andmicroarrays can be specifically designed to provide a diagnosticexpression profile for a particular disease or condition (e.g., sepsis).

Microarrays are prepared by selecting probes which comprise apolynucleotide sequence, and then immobilizing such probes to a solidsupport or surface. For example, the probes may comprise DNA sequences,RNA sequences, or copolymer sequences of DNA and RNA. The polynucleotidesequences of the probes may also comprise DNA and/or RNA analogues, orcombinations thereof. For example, the polynucleotide sequences of theprobes may be full or partial fragments of genomic DNA. Thepolynucleotide sequences of the probes may also be synthesizednucleotide sequences, such as synthetic oligonucleotide sequences. Theprobe sequences can be synthesized either enzymatically in vivo,enzymatically in vitro (e.g., by PCR), or non-enzymatically in vitro.

Probes used in the methods of the invention are preferably immobilizedto a solid support which may be either porous or non-porous. Forexample, the probes may be polynucleotide sequences which are attachedto a nitrocellulose or nylon membrane or filter covalently at either the3′ or the 5′ end of the polynucleotide. Such hybridization probes arewell known in the art (see, e.g., Sambrook, et al., Molecular Cloning: ALaboratory Manual (3rd Edition, 2001). Alternatively, the solid supportor surface may be a glass or plastic surface. In one embodiment,hybridization levels are measured to microarrays of probes consisting ofa solid phase on the surface of which are immobilized a population ofpolynucleotides, such as a population of DNA or DNA mimics, or,alternatively, a population of RNA or RNA mimics. The solid phase may bea nonporous or, optionally, a porous material such as a gel.

In one embodiment, the microarray comprises a support or surface with anordered array of binding (e.g., hybridization) sites or “probes” eachrepresenting one of the biomarkers described herein. Preferably themicroarrays are addressable arrays, and more preferably positionallyaddressable arrays. More specifically, each probe of the array ispreferably located at a known, predetermined position on the solidsupport such that the identity (i.e., the sequence) of each probe can bedetermined from its position in the array (i.e., on the support orsurface). Each probe is preferably covalently attached to the solidsupport at a single site.

Microarrays can be made in a number of ways, of which several aredescribed below. However they are produced, microarrays share certaincharacteristics. The arrays are reproducible, allowing multiple copiesof a given array to be produced and easily compared with each other.Preferably, microarrays are made from materials that are stable underbinding (e.g., nucleic acid hybridization) conditions. Microarrays aregenerally small, e.g., between 1 cm² and 25 cm²; however, larger arraysmay also be used, e.g., in screening arrays. Preferably, a given bindingsite or unique set of binding sites in the microarray will specificallybind (e.g., hybridize) to the product of a single gene in a cell (e.g.,to a specific mRNA, or to a specific cDNA derived therefrom). However,in general, other related or similar sequences will cross hybridize to agiven binding site.

As noted above, the “probe” to which a particular polynucleotidemolecule specifically hybridizes contains a complementary polynucleotidesequence. The probes of the microarray typically consist of nucleotidesequences of no more than 1,000 nucleotides. In some embodiments, theprobes of the array consist of nucleotide sequences of 10 to 1,000nucleotides. In one embodiment, the nucleotide sequences of the probesare in the range of 10-200 nucleotides in length and are genomicsequences of one species of organism, such that a plurality of differentprobes is present, with sequences complementary and thus capable ofhybridizing to the genome of such a species of organism, sequentiallytiled across all or a portion of the genome. In other embodiments, theprobes are in the range of 10-30 nucleotides in length, in the range of10-40 nucleotides in length, in the range of 20-50 nucleotides inlength, in the range of 40-80 nucleotides in length, in the range of50-150 nucleotides in length, in the range of 80-120 nucleotides inlength, or are 60 nucleotides in length.

The probes may comprise DNA or DNA “mimics” (e.g., derivatives andanalogues) corresponding to a portion of an organism's genome. Inanother embodiment, the probes of the microarray are complementary RNAor RNA mimics. DNA mimics are polymers composed of subunits capable ofspecific, Watson-Crick-like hybridization with DNA, or of specifichybridization with RNA. The nucleic acids can be modified at the basemoiety, at the sugar moiety, or at the phosphate backbone (e.g.,phosphorothioates).

DNA can be obtained, e.g., by polymerase chain reaction (PCR)amplification of genomic DNA or cloned sequences. PCR primers arepreferably chosen based on a known sequence of the genome that willresult in amplification of specific fragments of genomic DNA. Computerprograms that are well known in the art are useful in the design ofprimers with the required specificity and optimal amplificationproperties, such as Oligo version 5.0 (National Biosciences). Typicallyeach probe on the microarray will be between 10 bases and 50,000 bases,usually between 300 bases and 1,000 bases in length. PCR methods arewell known in the art, and are described, for example, in Innis et al.,eds., PCR Protocols: A Guide To Methods And Applications, Academic PressInc., San Diego, Calif. (1990); herein incorporated by reference in itsentirety. It will be apparent to one skilled in the art that controlledrobotic systems are useful for isolating and amplifying nucleic acids.

An alternative, preferred means for generating polynucleotide probes isby synthesis of synthetic polynucleotides or oligonucleotides, e.g.,using N-phosphonate or phosphoramidite chemistries (Froehler et al.,Nucleic Acid Res. 14:5399-5407 (1986); McBride et al., Tetrahedron Lett.24:246-248 (1983)). Synthetic sequences are typically between about 10and about 500 bases in length, more typically between about 20 and about100 bases, and most preferably between about 40 and about 70 bases inlength. In some embodiments, synthetic nucleic acids include non-naturalbases, such as, but by no means limited to, inosine. As noted above,nucleic acid analogues may be used as binding sites for hybridization.An example of a suitable nucleic acid analogue is peptide nucleic acid(see, e.g., Egholm et al., Nature 363:566-568 (1993); U.S. Pat. No.5,539,083).

Probes are preferably selected using an algorithm that takes intoaccount binding energies, base composition, sequence complexity,cross-hybridization binding energies, and secondary structure. SeeFriend et al., International Patent Publication WO 01/05935, publishedJan. 25, 2001; Hughes et al., Nat. Biotech. 19:342-7 (2001).

A skilled artisan will also appreciate that positive control probes,e.g., probes known to be complementary and hybridizable to sequences inthe target polynucleotide molecules, and negative control probes, e.g.,probes known to not be complementary and hybridizable to sequences inthe target polynucleotide molecules, should be included on the array. Inone embodiment, positive controls are synthesized along the perimeter ofthe array. In another embodiment, positive controls are synthesized indiagonal stripes across the array. In still another embodiment, thereverse complement for each probe is synthesized next to the position ofthe probe to serve as a negative control. In yet another embodiment,sequences from other species of organism are used as negative controlsor as “spike-in” controls.

The probes are attached to a solid support or surface, which may bemade, e.g., from glass, plastic (e.g., polypropylene, nylon),polyacrylamide, nitrocellulose, gel, or other porous or nonporousmaterial. One method for attaching nucleic acids to a surface is byprinting on glass plates, as is described generally by Schena et al,Science 270:467-470 (1995). This method is especially useful forpreparing microarrays of cDNA (See also, DeRisi et al, Nature Genetics14:457-460 (1996); Shalon et al., Genome Res. 6:639-645 (1996); andSchena et al., Proc. Natl. Acad. Sci. U.S.A. 93:10539-11286 (1995);herein incorporated by reference in their entireties).

A second method for making microarrays produces high-densityoligonucleotide arrays. Techniques are known for producing arrayscontaining thousands of oligonucleotides complementary to definedsequences, at defined locations on a surface using photolithographictechniques for synthesis in situ (see, Fodor et al., 1991, Science251:767-773; Pease et al., 1994, Proc. Natl. Acad. Sci. U.S.A.91:5022-5026; Lockhart et al., 1996, Nature Biotechnology 14:1675; U.S.Pat. Nos. 5,578,832; 5,556,752; and 5,510,270; herein incorporated byreference in their entireties) or other methods for rapid synthesis anddeposition of defined oligonucleotides (Blanchard et al., Biosensors &Bioelectronics 11:687-690; herein incorporated by reference in itsentirety). When these methods are used, oligonucleotides (e.g., 60-mers)of known sequence are synthesized directly on a surface such as aderivatized glass slide. Usually, the array produced is redundant, withseveral oligonucleotide molecules per RNA.

Other methods for making microarrays, e.g., by masking (Maskos andSouthern, 1992, Nuc. Acids. Res. 20:1679-1684; herein incorporated byreference in its entirety), may also be used. In principle, any type ofarray, for example, dot blots on a nylon hybridization membrane (seeSambrook, et al., Molecular Cloning: A Laboratory Manual, 3rd Edition,2001) could be used. However, as will be recognized by those skilled inthe art, very small arrays will frequently be preferred becausehybridization volumes will be smaller.

Microarrays can also be manufactured by means of an ink jet printingdevice for oligonucleotide synthesis, e.g., using the methods andsystems described by Blanchard in U.S. Pat. No. 6,028,189; Blanchard etal., 1996, Biosensors and Bioelectronics 11:687-690; Blanchard, 1998, inSynthetic DNA Arrays in Genetic Engineering, Vol. 20, J. K. Setlow, Ed.,Plenum Press, New York at pages 111-123; herein incorporated byreference in their entireties. Specifically, the oligonucleotide probesin such microarrays are synthesized in arrays, e.g., on a glass slide,by serially depositing individual nucleotide bases in “microdroplets” ofa high surface tension solvent such as propylene carbonate. Themicrodroplets have small volumes (e.g., 100 pL or less, more preferably50 pL or less) and are separated from each other on the microarray(e.g., by hydrophobic domains) to form circular surface tension wellswhich define the locations of the array elements (i.e., the differentprobes). Microarrays manufactured by this ink-jet method are typicallyof high density, preferably having a density of at least about 2,500different probes per 1 cm². The polynucleotide probes are attached tothe support covalently at either the 3′ or the 5′ end of thepolynucleotide.

Biomarker polynucleotides which may be measured by microarray analysiscan be expressed RNA or a nucleic acid derived therefrom (e.g., cDNA oramplified RNA derived from cDNA that incorporates an RNA polymerasepromoter), including naturally occurring nucleic acid molecules, as wellas synthetic nucleic acid molecules. In one embodiment, the targetpolynucleotide molecules comprise RNA, including, but by no meanslimited to, total cellular RNA, poly(A)⁺ messenger RNA (mRNA) or afraction thereof, cytoplasmic mRNA, or RNA transcribed from cDNA (i.e.,cRNA; see, e.g., Linsley & Schelter, U.S. patent application Ser. No.09/411,074, filed Oct. 4, 1999, or U.S. Pat. No. 5,545,522, 5,891,636,or 5,716,785). Methods for preparing total and poly(A)⁺ RNA are wellknown in the art, and are described generally, e.g., in Sambrook, etal., Molecular Cloning: A Laboratory Manual (3rd Edition, 2001). RNA canbe extracted from a cell of interest using guanidinium thiocyanate lysisfollowed by CsCl centrifugation (Chirgwin et al., 1979, Biochemistry18:5294-5299), a silica gel-based column (e.g., RNeasy (Qiagen,Valencia, Calif.) or StrataPrep (Stratagene, La Jolla, Calif.)), orusing phenol and chloroform, as described in Ausubel et al., eds., 1989,Current Protocols In Molecular Biology, Vol. III, Green PublishingAssociates, Inc., John Wiley & Sons, Inc., New York, at pp.13.12.1-13.12.5). Poly(A)⁺ RNA can be selected, e.g., by selection witholigo-dT cellulose or, alternatively, by oligo-dT primed reversetranscription of total cellular RNA. RNA can be fragmented by methodsknown in the art, e.g., by incubation with ZnCl₂, to generate fragmentsof RNA.

In one embodiment, total RNA, mRNA, or nucleic acids derived therefrom,are isolated from a sample taken from a patient suspected of having alife-threatening condition (e.g., sepsis, severe trauma, or burn).Biomarker polynucleotides that are poorly expressed in particular cellsmay be enriched using normalization techniques (Bonaldo et al., 1996,Genome Res. 6:791-806).

As described above, the biomarker polynucleotides can be detectablylabeled at one or more nucleotides. Any method known in the art may beused to label the target polynucleotides. Preferably, this labelingincorporates the label uniformly along the length of the RNA, and morepreferably, the labeling is carried out at a high degree of efficiency.For example, polynucleotides can be labeled by oligo-dT primed reversetranscription. Random primers (e.g., 9-mers) can be used in reversetranscription to uniformly incorporate labeled nucleotides over the fulllength of the polynucleotides. Alternatively, random primers may be usedin conjunction with PCR methods or T7 promoter-based in vitrotranscription methods in order to amplify polynucleotides.

The detectable label may be a luminescent label. For example,fluorescent labels, bioluminescent labels, chemiluminescent labels, andcolorimetric labels may be used in the practice of the invention.Fluorescent labels that can be used include, but are not limited to,fluorescein, a phosphor, a rhodamine, or a polymethine dye derivative.Additionally, commercially available fluorescent labels including, butnot limited to, fluorescent phosphoramidites such as FluorePrime(Amersham Pharmacia, Piscataway, N.J.), Fluoredite (Miilipore, Bedford,Mass.), FAM (ABI, Foster City, Calif.), and Cy3 or Cy5 (AmershamPharmacia, Piscataway, N.J.) can be used. Alternatively, the detectablelabel can be a radiolabeled nucleotide.

In one embodiment, biomarker polynucleotide molecules from a patientsample are labeled differentially from the corresponding polynucleotidemolecules of a reference sample. The reference can comprisepolynucleotide molecules from a normal biological sample (i.e., controlsample, e.g., blood from a survivor or a subject not havingsepsis/infection, burn, or trauma) or from a reference biologicalsample, (e.g., blood from a non-survivor or a subject havingsepsis/infection, burn, or trauma).

Nucleic acid hybridization and wash conditions are chosen so that thetarget polynucleotide molecules specifically bind or specificallyhybridize to the complementary polynucleotide sequences of the array,preferably to a specific array site, wherein its complementary DNA islocated. Arrays containing double-stranded probe DNA situated thereonare preferably subjected to denaturing conditions to render the DNAsingle-stranded prior to contacting with the target polynucleotidemolecules. Arrays containing single-stranded probe DNA (e.g., syntheticoligodeoxyribonucleic acids) may need to be denatured prior tocontacting with the target polynucleotide molecules, e.g., to removehairpins or dimers which form due to self-complementary sequences.

Optimal hybridization conditions will depend on the length (e.g.,oligomer versus polynucleotide greater than 200 bases) and type (e.g.,RNA, or DNA) of probe and target nucleic acids. One of skill in the artwill appreciate that as the oligonucleotides become shorter, it maybecome necessary to adjust their length to achieve a relatively uniformmelting temperature for satisfactory hybridization results. Generalparameters for specific (i.e., stringent) hybridization conditions fornucleic acids are described in Sambrook, et al., Molecular Cloning: ALaboratory Manual (3rd Edition, 2001), and in Ausubel et al., CurrentProtocols In Molecular Biology, vol. 2, Current Protocols Publishing,New York (1994). Typical hybridization conditions for the cDNAmicroarrays of Schena et al. are hybridization in 5.times.SSC plus 0.2%SDS at 65° C. for four hours, followed by washes at 25° C. in lowstringency wash buffer (1×SSC plus 0.2% SDS), followed by 10 minutes at25° C. in higher stringency wash buffer (0.1×SSC plus 0.2% SDS) (Schenaet al., Proc. Natl. Acad. Sci. U.S.A. 93:10614 (1993)). Usefulhybridization conditions are also provided in, e.g., Tijessen, 1993,Hybridization With Nucleic Acid Probes, Elsevier Science PublishersB.V.; and Kricka, 1992, Nonisotopic Dna Probe Techniques, AcademicPress, San Diego, Calif. Particularly preferred hybridization conditionsinclude hybridization at a temperature at or near the mean meltingtemperature of the probes (e.g., within 51° C., more preferably within21° C.) in 1 M NaCl, 50 mM MES buffer (pH 6.5), 0.5% sodium sarcosineand 30% formamide.

When fluorescently labeled gene products are used, the fluorescenceemissions at each site of a microarray may be, preferably, detected byscanning confocal laser microscopy. In one embodiment, a separate scan,using the appropriate excitation line, is carried out for each of thetwo fluorophores used. Alternatively, a laser may be used that allowssimultaneous specimen illumination at wavelengths specific to the twofluorophores and emissions from the two fluorophores can be analyzedsimultaneously (see Shalon et al., 1996, “A DNA microarray system foranalyzing complex DNA samples using two-color fluorescent probehybridization,” Genome Research 6:639-645, which is incorporated byreference in its entirety for all purposes). Arrays can be scanned witha laser fluorescent scanner with a computer controlled X-Y stage and amicroscope objective. Sequential excitation of the two fluorophores isachieved with a multi-line, mixed gas laser and the emitted light issplit by wavelength and detected with two photomultiplier tubes.Fluorescence laser scanning devices are described in Schena et al.,Genome Res. 6:639-645 (1996), and in other references cited herein.Alternatively, the fiber-optic bundle described by Ferguson et al.,Nature Biotech. 14:1681-1684 (1996), may be used to monitor mRNAabundance levels at a large number of sites simultaneously.

In one embodiment, the invention includes a microarray comprising anoligonucleotide that hybridizes to an oligonucleotide that hybridizes toa DEFA4 polynucleotide, an oligonucleotide that hybridizes to a CD163polynucleotide, an oligonucleotide that hybridizes to a PER1polynucleotide, an oligonucleotide that hybridizes to a RGS1polynucleotide, an oligonucleotide that hybridizes to an HIF1Apolynucleotide, an oligonucleotide that hybridizes to a SEPP1polynucleotide, an oligonucleotide that hybridizes to a C11orf74polynucleotide, an oligonucleotide that hybridizes to a CITpolynucleotide, an oligonucleotide that hybridizes to a LY86polynucleotide, an oligonucleotide that hybridizes to a TSTpolynucleotide, an oligonucleotide that hybridizes to an OR52R1polynucleotide, and an oligonucleotide that hybridizes to a KCNJ2polynucleotide.

Polynucleotides can also be analyzed by other methods including, but notlimited to, northern blotting, nuclease protection assays, RNAfingerprinting, polymerase chain reaction, ligase chain reaction, Qbetareplicase, isothermal amplification method, strand displacementamplification, transcription based amplification systems, nucleaseprotection (Si nuclease or RNAse protection assays), SAGE as well asmethods disclosed in International Publication Nos. WO 88/10315 and WO89/06700, and International Applications Nos. PCT/US87/00880 andPCT/US89/01025; herein incorporated by reference in their entireties.

A standard Northern blot assay can be used to ascertain an RNAtranscript size, identify alternatively spliced RNA transcripts, and therelative amounts of mRNA in a sample, in accordance with conventionalNorthern hybridization techniques known to those persons of ordinaryskill in the art. In Northern blots, RNA samples are first separated bysize by electrophoresis in an agarose gel under denaturing conditions.The RNA is then transferred to a membrane, cross-linked, and hybridizedwith a labeled probe. Nonisotopic or high specific activity radiolabeledprobes can be used, including random-primed, nick-translated, orPCR-generated DNA probes, in vitro transcribed RNA probes, andoligonucleotides. Additionally, sequences with only partial homology(e.g., cDNA from a different species or genomic DNA fragments that mightcontain an exon) may be used as probes. The labeled probe, e.g., aradiolabelled cDNA, either containing the full-length, single strandedDNA or a fragment of that DNA sequence may be at least 20, at least 30,at least 50, or at least 100 consecutive nucleotides in length. Theprobe can be labeled by any of the many different methods known to thoseskilled in this art. The labels most commonly employed for these studiesare radioactive elements, enzymes, chemicals that fluoresce when exposedto ultraviolet light, and others. A number of fluorescent materials areknown and can be utilized as labels. These include, but are not limitedto, fluorescein, rhodamine, auramine, Texas Red, AMCA blue and LuciferYellow. A particular detecting material is anti-rabbit antibody preparedin goats and conjugated with fluorescein through an isothiocyanate.Proteins can also be labeled with a radioactive element or with anenzyme. The radioactive label can be detected by any of the currentlyavailable counting procedures. Isotopes that can be used include, butare not limited to, ³H, ¹⁴C, ³²P, ³⁵S, ³⁶Cl, ³⁵Cr, ⁵⁷Co, ⁵⁸Co, ⁵⁹Fe,⁹⁰Y, ¹²⁵I, ¹³¹I, and ¹⁸⁶Re. Enzyme labels are likewise useful, and canbe detected by any of the presently utilized colorimetric,spectrophotometric, fluorospectrophotometric, amperometric or gasometrictechniques. The enzyme is conjugated to the selected particle byreaction with bridging molecules such as carbodiimides, diisocyanates,glutaraldehyde and the like. Any enzymes known to one of skill in theart can be utilized. Examples of such enzymes include, but are notlimited to, peroxidase, beta-D-galactosidase, urease, glucose oxidaseplus peroxidase and alkaline phosphatase. U.S. Pat. Nos. 3,654,090,3,850,752, and 4,016,043 are referred to by way of example for theirdisclosure of alternate labeling material and methods.

Nuclease protection assays (including both ribonuclease protectionassays and Si nuclease assays) can be used to detect and quantitatespecific mRNAs. In nuclease protection assays, an antisense probe(labeled with, e.g., radiolabeled or nonisotopic) hybridizes in solutionto an RNA sample. Following hybridization, single-stranded, unhybridizedprobe and RNA are degraded by nucleases. An acrylamide gel is used toseparate the remaining protected fragments. Typically, solutionhybridization is more efficient than membrane-based hybridization, andit can accommodate up to 100 μg of sample RNA, compared with the 20-30μg maximum of blot hybridizations.

The ribonuclease protection assay, which is the most common type ofnuclease protection assay, requires the use of RNA probes.Oligonucleotides and other single-stranded DNA probes can only be usedin assays containing 51 nuclease. The single-stranded, antisense probemust typically be completely homologous to target RNA to preventcleavage of the probe:target hybrid by nuclease.

Serial Analysis Gene Expression (SAGE) can also be used to determine RNAabundances in a cell sample. See, e.g., Velculescu et al., 1995, Science270:484-7; Carulli, et al., 1998, Journal of Cellular BiochemistrySupplements 30/31:286-96; herein incorporated by reference in theirentireties. SAGE analysis does not require a special device fordetection, and is one of the preferable analytical methods forsimultaneously detecting the expression of a large number oftranscription products. First, poly A⁺ RNA is extracted from cells.Next, the RNA is converted into cDNA using a biotinylated oligo (dT)primer, and treated with a four-base recognizing restriction enzyme(Anchoring Enzyme: AE) resulting in AE-treated fragments containing abiotin group at their 3′ terminus. Next, the AE-treated fragments areincubated with streptavidin for binding. The bound cDNA is divided intotwo fractions, and each fraction is then linked to a differentdouble-stranded oligonucleotide adapter (linker) A or B. These linkersare composed of: (1) a protruding single strand portion having asequence complementary to the sequence of the protruding portion formedby the action of the anchoring enzyme, (2) a 5′ nucleotide recognizingsequence of the ITS-type restriction enzyme (cleaves at a predeterminedlocation no more than 20 bp away from the recognition site) serving as atagging enzyme (TE), and (3) an additional sequence of sufficient lengthfor constructing a PCR-specific primer. The linker-linked cDNA iscleaved using the tagging enzyme, and only the linker-linked cDNAsequence portion remains, which is present in the form of a short-strandsequence tag. Next, pools of short-strand sequence tags from the twodifferent types of linkers are linked to each other, followed by PCRamplification using primers specific to linkers A and B. As a result,the amplification product is obtained as a mixture comprising myriadsequences of two adjacent sequence tags (ditags) bound to linkers A andB. The amplification product is treated with the anchoring enzyme, andthe free ditag portions are linked into strands in a standard linkagereaction. The amplification product is then cloned. Determination of theclone's nucleotide sequence can be used to obtain a read-out ofconsecutive ditags of constant length. The presence of mRNAcorresponding to each tag can then be identified from the nucleotidesequence of the clone and information on the sequence tags.

Quantitative reverse transcriptase PCR (qRT-PCR) can also be used todetermine the expression profiles of biomarkers (see, e.g., U.S. PatentApplication Publication No. 2005/0048542A1; herein incorporated byreference in its entirety). The first step in gene expression profilingby RT-PCR is the reverse transcription of the RNA template into cDNA,followed by its exponential amplification in a PCR reaction. The twomost commonly used reverse transcriptases are avilo myeloblastosis virusreverse transcriptase (AMV-RT) and Moloney murine leukemia virus reversetranscriptase (MLV-RT). The reverse transcription step is typicallyprimed using specific primers, random hexamers, or oligo-dT primers,depending on the circumstances and the goal of expression profiling. Forexample, extracted RNA can be reverse-transcribed using a GeneAmp RNAPCR kit (Perkin Elmer, Calif., USA), following the manufacturer'sinstructions. The derived cDNA can then be used as a template in thesubsequent PCR reaction.

Although the PCR step can use a variety of thermostable DNA-dependentDNA polymerases, it typically employs the Taq DNA polymerase, which hasa 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonucleaseactivity. Thus, TAQMAN PCR typically utilizes the 5′-nuclease activityof Taq or Tth polymerase to hydrolyze a hybridization probe bound to itstarget amplicon, but any enzyme with equivalent 5′ nuclease activity canbe used. Two oligonucleotide primers are used to generate an amplicontypical of a PCR reaction. A third oligonucleotide, or probe, isdesigned to detect nucleotide sequence located between the two PCRprimers. The probe is non-extendible by Taq DNA polymerase enzyme, andis labeled with a reporter fluorescent dye and a quencher fluorescentdye. Any laser-induced emission from the reporter dye is quenched by thequenching dye when the two dyes are located close together as they areon the probe. During the amplification reaction, the Taq DNA polymeraseenzyme cleaves the probe in a template-dependent manner. The resultantprobe fragments disassociate in solution, and signal from the releasedreporter dye is free from the quenching effect of the secondfluorophore. One molecule of reporter dye is liberated for each newmolecule synthesized, and detection of the unquenched reporter dyeprovides the basis for quantitative interpretation of the data.

TAQMAN RT-PCR can be performed using commercially available equipment,such as, for example, ABI PRISM 7700 sequence detection system.(Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), orLightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In apreferred embodiment, the 5′ nuclease procedure is run on a real-timequantitative PCR device such as the ABI PRISM 7700 sequence detectionsystem. The system consists of a thermocycler, laser, charge-coupleddevice (CCD), camera and computer. The system includes software forrunning the instrument and for analyzing the data. 5′-Nuclease assaydata are initially expressed as Ct, or the threshold cycle. Fluorescencevalues are recorded during every cycle and represent the amount ofproduct amplified to that point in the amplification reaction. The pointwhen the fluorescent signal is first recorded as statisticallysignificant is the threshold cycle (Ct).

To minimize errors and the effect of sample-to-sample variation, RT-PCRis usually performed using an internal standard. The ideal internalstandard is expressed at a constant level among different tissues, andis unaffected by the experimental treatment. RNAs most frequently usedto normalize patterns of gene expression are mRNAs for the housekeepinggenes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and beta-actin.

A more recent variation of the RT-PCR technique is the real timequantitative PCR, which measures PCR product accumulation through adual-labeled fluorigenic probe (i.e., TAQMAN probe). Real time PCR iscompatible both with quantitative competitive PCR, where internalcompetitor for each target sequence is used for normalization, and withquantitative comparative PCR using a normalization gene contained withinthe sample, or a housekeeping gene for RT-PCR. For further details see,e.g. Held et al., Genome Research 6:986-994 (1996).

Analysis of Biomarker Data

Biomarker data may be analyzed by a variety of methods to identifybiomarkers and determine the statistical significance of differences inobserved levels of biomarkers between test and reference expressionprofiles in order to evaluate whether a patient is at risk of mortalitywithin 30 days. In certain embodiments, patient data is analyzed by oneor more methods including, but not limited to, multivariate lineardiscriminant analysis (LDA), receiver operating characteristic (ROC)analysis, principal component analysis (PCA), ensemble data miningmethods, significance analysis of microarrays (SAM), cell specificsignificance analysis of microarrays (csSAM), spanning-tree progressionanalysis of density-normalized events (SPADE), and multi-dimensionalprotein identification technology (MUDPIT) analysis. (See, e.g., Hilbe(2009) Logistic Regression Models, Chapman & Hall/CRC Press; McLachlan(2004) Discriminant Analysis and Statistical Pattern Recognition. WileyInterscience; Zweig et al. (1993) Clin. Chem. 39:561-577; Pepe (2003)The statistical evaluation of medical tests for classification andprediction, New York, N.Y.: Oxford; Sing et al. (2005) Bioinformatics21:3940-3941; Tusher et al. (2001) Proc. Natl. Acad. Sci. U.S.A.98:5116-5121; Oza (2006) Ensemble data mining, NASA Ames ResearchCenter, Moffett Field, Calif., USA; English et al. (2009) J. Biomed.Inform. 42(2):287-295; Zhang (2007) Bioinformatics 8: 230; Shen-Orr etal. (2010) Journal of Immunology 184:144-130; Qiu et al. (2011) Nat.Biotechnol. 29(10):886-891; Ru et al. (2006) J. Chromatogr. A.1111(2):166-174, Jolliffe Principal Component Analysis (Springer Seriesin Statistics, 2^(nd) edition, Springer, N Y, 2002), Koren et al. (2004)IEEE Trans Vis Comput Graph 10:459-470; herein incorporated by referencein their entireties.)

C. Kits

In yet another aspect, the invention provides kits for prognosis ofmortality in a subject, wherein the kits can be used to detect thebiomarkers of the present invention. For example, the kits can be usedto detect any one or more of the biomarkers described herein, which aredifferentially expressed in samples from survivors and non-survivors incritically ill patients. The kit may include one or more agents fordetection of biomarkers, a container for holding a biological sampleisolated from a human subject suspected of having a life-threateningcondition; and printed instructions for reacting agents with thebiological sample or a portion of the biological sample to detect thepresence or amount of at least one biomarker in the biological sample.The agents may be packaged in separate containers. The kit may furthercomprise one or more control reference samples and reagents forperforming an immunoassay or microarray analysis.

In certain embodiments, the kit comprises agents for measuring thelevels of at least twelve biomarkers of interest. For example, the kitmay include agents for detecting biomarkers of a panel comprising aDEFA4 polynucleotide, a CD163 polynucleotide, a PER1 polynucleotide, aRGS1 polynucleotide, an HIF1A polynucleotide, a SEPP1 polynucleotide, aC11orf74 polynucleotide, a CIT polynucleotide, LY86 polynucleotide, aTST polynucleotide, an OR52R1 polynucleotide, and a KCNJ2polynucleotide.

In certain embodiments, the kit comprises a microarray for analysis of aplurality of biomarker polynucleotides. An exemplary microarray includedin the kit comprises an oligonucleotide that hybridizes to a DEFA4polynucleotide, an oligonucleotide that hybridizes to a CD163polynucleotide, an oligonucleotide that hybridizes to a PER1polynucleotide, an oligonucleotide that hybridizes to a RGS1polynucleotide, an oligonucleotide that hybridizes to an HIF1Apolynucleotide, an oligonucleotide that hybridizes to a SEPP1polynucleotide, an oligonucleotide that hybridizes to a C11orf74polynucleotide, an oligonucleotide that hybridizes to a CITpolynucleotide, an oligonucleotide that hybridizes to a LY86polynucleotide, an oligonucleotide that hybridizes to a TSTpolynucleotide, an oligonucleotide that hybridizes to an OR52R1polynucleotide, and an oligonucleotide that hybridizes to a KCNJ2polynucleotide.

The kit can comprise one or more containers for compositions containedin the kit. Compositions can be in liquid form or can be lyophilized.Suitable containers for the compositions include, for example, bottles,vials, syringes, and test tubes. Containers can be formed from a varietyof materials, including glass or plastic. The kit can also comprise apackage insert containing written instructions for methods of diagnosingsepsis.

The kits of the invention have a number of applications. For example,the kits can be used to determine the mortality risk of a subjectsuspected of having a life-threatening condition. Subjects identified ashaving a high risk of mortality within 30 days by the methods describedherein can be sent to the ICU for treatment, whereas patients identifiedas having a low risk of mortality within 30 days may be furthermonitored and/or treated in a regular hospital ward. Both patients andclinicians can benefit from better estimates of mortality risk, whichallows timely discussions of patient preferences and their choicesregarding life-saving measures. Better molecular phenotyping of patientsalso makes possible improvements in clinical trials, both in 1) patientselection for drugs and interventions and 2) assessment ofobserved-to-expected ratios of subject mortality.

D. Diagnostic System and Computerized Methods for Determining MortalityRisk

In a further aspect, the invention includes a computer implementedmethod for determining mortality risk of a patient suspected of having alife-threatening condition. The computer performs steps comprising:receiving inputted patient data comprising values for the levels of oneor more biomarkers in a biological sample from the patient; analyzingthe levels of one or more biomarkers and comparing with respectivereference value ranges for the biomarkers; calculating a mortality genescore for the patient based on the levels of the biomarkers, wherein ahigher mortality gene score for the patient compared to a controlsubject indicates that the patient is at high risk of mortality within30 days; and displaying information regarding the mortality risk of thepatient. In certain embodiments, the inputted patient data comprisesvalues for the levels of a plurality of biomarkers in a biologicalsample from the patient. In one embodiment, the inputted patient datacomprises values for the levels of DEFA4, CD163, PER1, RGS1, HIF1A,SEPP1, C11orf74, CIT, LY86, TST, OR52R1, and KCNJ2 polynucleotides.

In a further aspect, the invention includes a diagnostic system forperforming the computer implemented method, as described. A diagnosticsystem may include a computer containing a processor, a storagecomponent (i.e., memory), a display component, and other componentstypically present in general purpose computers. The storage componentstores information accessible by the processor, including instructionsthat may be executed by the processor and data that may be retrieved,manipulated or stored by the processor.

The storage component includes instructions for determining themortality risk of the subject. For example, the storage componentincludes instructions for calculating the mortality gene score for thesubject based on biomarker expression levels, as described herein (e.g.,see Example 1). In addition, the storage component may further compriseinstructions for performing multivariate linear discriminant analysis(LDA), receiver operating characteristic (ROC) analysis, principalcomponent analysis (PCA), ensemble data mining methods, cell specificsignificance analysis of microarrays (csSAM), or multi-dimensionalprotein identification technology (MUDPIT) analysis. The computerprocessor is coupled to the storage component and configured to executethe instructions stored in the storage component in order to receivepatient data and analyze patient data according to one or morealgorithms. The display component displays information regarding thediagnosis and/or prognosis (e.g., mortality risk) of the patient.

The storage component may be of any type capable of storing informationaccessible by the processor, such as a hard-drive, memory card, ROM,RAM, DVD, CD-ROM, USB Flash drive, write-capable, and read-onlymemories. The processor may be any well-known processor, such asprocessors from Intel Corporation. Alternatively, the processor may be adedicated controller such as an ASIC.

The instructions may be any set of instructions to be executed directly(such as machine code) or indirectly (such as scripts) by the processor.In that regard, the terms “instructions,” “steps” and “programs” may beused interchangeably herein. The instructions may be stored in objectcode form for direct processing by the processor, or in any othercomputer language including scripts or collections of independent sourcecode modules that are interpreted on demand or compiled in advance.

Data may be retrieved, stored or modified by the processor in accordancewith the instructions. For instance, although the diagnostic system isnot limited by any particular data structure, the data may be stored incomputer registers, in a relational database as a table having aplurality of different fields and records, XML documents, or flat files.The data may also be formatted in any computer-readable format such as,but not limited to, binary values, ASCII or Unicode. Moreover, the datamay comprise any information sufficient to identify the relevantinformation, such as numbers, descriptive text, proprietary codes,pointers, references to data stored in other memories (including othernetwork locations) or information which is used by a function tocalculate the relevant data.

In certain embodiments, the processor and storage component may comprisemultiple processors and storage components that may or may not be storedwithin the same physical housing. For example, some of the instructionsand data may be stored on removable CD-ROM and others within a read-onlycomputer chip. Some or all of the instructions and data may be stored ina location physically remote from, yet still accessible by, theprocessor. Similarly, the processor may actually comprise a collectionof processors which may or may not operate in parallel.

In one aspect, computer is a server communicating with one or moreclient computers. Each client computer may be configured similarly tothe server, with a processor, storage component and instructions. Eachclient computer may be a personal computer, intended for use by aperson, having all the internal components normally found in a personalcomputer such as a central processing unit (CPU), display (for example,a monitor displaying information processed by the processor), CD-ROM,hard-drive, user input device (for example, a mouse, keyboard,touch-screen or microphone), speakers, modem and/or network interfacedevice (telephone, cable or otherwise) and all of the components usedfor connecting these elements to one another and permitting them tocommunicate (directly or indirectly) with one another. Moreover,computers in accordance with the systems and methods described hereinmay comprise any device capable of processing instructions andtransmitting data to and from humans and other computers includingnetwork computers lacking local storage capability.

Although the client computers and may comprise a full-sized personalcomputer, many aspects of the system and method are particularlyadvantageous when used in connection with mobile devices capable ofwirelessly exchanging data with a server over a network such as theInternet. For example, client computer may be a wireless-enabled PDAsuch as a Blackberry phone, Apple iPhone, Android phone, or otherInternet-capable cellular phone. In such regard, the user may inputinformation using a small keyboard, a keypad, a touch screen, or anyother means of user input. The computer may have an antenna forreceiving a wireless signal.

The server and client computers are capable of direct and indirectcommunication, such as over a network. Although only a few computers maybe used, it should be appreciated that a typical system can include alarge number of connected computers, with each different computer beingat a different node of the network. The network, and intervening nodes,may comprise various combinations of devices and communication protocolsincluding the Internet, World Wide Web, intranets, virtual privatenetworks, wide area networks, local networks, cell phone networks,private networks using communication protocols proprietary to one ormore companies, Ethernet, WiFi and HTTP. Such communication may befacilitated by any device capable of transmitting data to and from othercomputers, such as modems (e.g., dial-up or cable), networks andwireless interfaces. The server may be a web server.

Although certain advantages are obtained when information is transmittedor received as noted above, other aspects of the system and method arenot limited to any particular manner of transmission of information. Forexample, in some aspects, information may be sent via a medium such as adisk, tape, flash drive, DVD, or CD-ROM. In other aspects, theinformation may be transmitted in a non-electronic format and manuallyentered into the system. Yet further, although some functions areindicated as taking place on a server and others on a client, variousaspects of the system and method may be implemented by a single computerhaving a single processor.

III. EXPERIMENTAL

Below are examples of specific embodiments for carrying out the presentinvention. The examples are offered for illustrative purposes only, andare not intended to limit the scope of the present invention in any way.

Efforts have been made to ensure accuracy with respect to numbers used(e.g., amounts, temperatures, etc.), but some experimental error anddeviation should, of course, be allowed for.

Example 1 Methods for Prognosis of Mortality in Critically Ill Patients

Introduction

We have previously shown that multi-cohort analysis is an effective wayto find conserved gene expression signals in sepsis¹⁵ and other diseasesincluding organ transplant rejection, viral infections, and pulmonarytuberculosis¹⁶⁻¹⁸. We thus hypothesized that a multi-cohort analysisexamining outcomes in patients with sepsis would yield a conserved geneset that could robustly predict sepsis outcomes, and potentially be usedas a clinical tool for disease prognosis.

Materials and Methods

Study Design

The purpose of this study was to use an integrated multi-cohortmeta-analysis framework to analyze multiple gene expression datasets toidentify a set of genes that can predict mortality in patients withsepsis, at the time of admission. This framework has been describedpreviouslyl^(15,16,18).

Search

Two public gene expression microarray repositories (NIH GEO,ArrayExpress) were searched for all human datasets that matched any ofthe following search terms: sepsis, SIRS, trauma, shock, surgery,infection, pneumonia, critical, ICU, inflammatory, nosocomial. Datasetsthat compared either healthy controls or patients with non-infectiousinflammation (SIRS, trauma, surgery, autoimmunity) to patients withacute infections and/or sepsis were kept for further study. Datasetsthat utilized endotoxin injection as a model for SIRS or sepsis were notincluded. Datasets done in sorted cells (PBMCs, neutrophils, etc.), wereexcluded.

In many cases, mortality and severity phenotypes were not available inthe public data; these authors were contacted for further data. Thisincluded datasets E-MTAB-1548¹², GSE10474¹⁹, GSE21802²⁰, GSE32707²¹,GSE33341²², GSE63042¹¹, GSE63990²³, GSE66099^(10,15,24,25), andGSE66890²⁶. In all cases, gene expression data are publicly available.The investigators who contributed these data did not participate in thedata analysis described below.

The two cohorts E-MTAB-4421 and E-MTAB-4451 came from the same study(GaINs)¹⁴, with the same inclusion/exclusion criteria, and wereprocessed on the same microarrays. Thus, after re-normalizing from rawdata, we used ComBat normalization to co-normalize these two cohortsinto a single cohort, which we refer to as E-MTAB-4421.51.

Glue Grant Data

In addition to the publicly-available datasets, we used the Inflammationand Host Response to Injury Program (Glue Grant) trauma datasets²⁷⁻²⁹.The Glue Grant datasets contain two cohorts: patients admitted withsevere trauma, and patients admitted with severe burns. The traumacohorts further include two sub-cohorts, one which sampled buffy coat,and the other which sampled sorted cells; the sorted-cells cohort wereexcluded from further study. Inclusion criteria are describedelsewhere³⁰. Trauma patients were sampled at the following days afteradmission: 0.5, 1, 4, 7, 14, 21, 28 days; Burn patients were sampled atadmission, and then at the time of their burn operations. The Glue Grantpatients were classified as ‘infected’ if they had a nosocomialinfection (pneumonia, urinary tract infection, catheter-relatedbloodstream infection, etc.), a surgical infection (excludingsuperficial wound infections), or underwent surgery for perforatedviscus; infection definitions can be found atgluegrant.org/commonlyreferencedpubs.htm. Samples drawn within +/−24hours of the day of diagnosis of infection were considered to be time ofinfection. Use of the Glue Grant was approved by both the Glue GrantConsortium and the Stanford University IRB (protocol 29798).

Gene Expression Normalization

All Affymetrix datasets were downloaded as CEL files and re-normalizedusing gcRMA (R package affy). Output from other arrays werenormal-exponential background corrected and then between-arrays quantilenormalized (R package limma). For all gene analyses, the mean of probesfor common genes was set as the gene expression level. All probe-to-genemappings were downloaded from GEO from the most current SOFT files.

Multi-Cohort Analysis

We performed a multi-cohort analysis^(15,16,18) of gene expression insepsis patients within 48 hours of admission comparing patients who diedwithin 30 days to patients who did not. In the rare case where we hadinformation on which patients died after 30 days, these patients wereexcluded. After selecting the input datasets, we combined effect sizeswithin cohorts using Hedges' g, and then evaluated summary effects witha DerSimonian-Laird meta-analysis. Significance thresholds were set at afalse discovery rate (FDR) of 0.05, with a summary effect size greaterthan 1.3 fold (in non-log space).

We next performed a meta-regression analysis in the cohorts whichsupplied phenotype data of clinical severity and age. For each cohort,for each gene, the model was a regression on mortality (dependent) as afunction of clinical severity plus age plus gene expression level. Tokeep the scales between datasets similar, (1) all clinical severityscores were converted to log-odds mortality, based on models in theirdescribing papers, and (2) all datasets were ComBat-normalized togetherprior to meta-analysis (this method resets the location and scale ofeach gene, but within-cohort differences are preserved). Themeta-regression was carried out using the closed-form method-of-momentsrandom-effects model variation³¹ of the synthesis-of-slopes regressionmethod described by Becker and Wu (2007)³². Thus, in this case, a genewas considered to be significant if it had statistically conservedregression coefficients (betas) across all cohorts for the prediction ofmortality independent of clinical severity and age. An uncorrected pvalue<0.01 was deemed significant.

In the final step of the analysis, we took as significant the union ofthe gene sets deemed to be significant both by standard multi-cohortanalysis and by meta-regression. These genes were then used in a greedyiterated search model, where a greedy forward search was allowed to runto completion, followed by a greedy backward search, and then anothergreedy forward search. This method iterated until it reached a stablegene set. Only the discovery datasets were used in the search, and thefunctions maximized the weighted AUC, which is the sum of the AUC ofeach discovery dataset multiplied by its sample size.

Gene Score

In the greedy search, and with the final gene set, the gene score isdefined as the geometric mean of the gene expression level for allpositive genes minus the geometric mean of the gene expression level ofall negative genes multiplied by the ratio of counts of positive tonegative genes. This was calculated for each sample in a datasetseparately. Genes not present in an entire dataset were excluded; genesmissing for individual samples were set to one.

ROC Curves

Class discriminatory power was examined comparing the gene scores forclasses of interest in each examined dataset. ROC curves of the genescore were constructed within datasets, and the area under the curve(AUC) was calculated using the trapezoidal method. Summary ROC curveswere calculated via the method of Kester and Buntinx, as previouslydescribed^(18,33). Summary curves were only used to summarize data fromsimilar comparisons (i.e., sepsis patients at admission).

Comparison with Severity Scores

We compared the prognostic power of the gene score with the prognosticpower of the clinical severity scores in all cohorts which containedthis information. To do these calculations, we first performed logisticregression on either the clinical severity score or the gene score topredict mortality. We then tested a combined model (mortality as afunction of clinical severity and gene score, without interaction term)and measured the AUC of the combined model.

Validation

The main validation examined patients admitted to the hospital withsepsis, comparing survivors with eventual non-survivors, as determinedby each study's original investigators. In the case of datasets withlongitudinal data, only the patients within the first 24-48 hours sinceadmission were used in the summary ROC analysis. ROC plots wereconstructed separately for patients at later time points, broken intobins by day since admission, depending on each study's sampling schema.

Custom analyses were used to study the performance of the gene set inthe Glue Grant datasets. First, all patients in each dataset (trauma andburns) were plotted by day since injury for both gene score and dailyseverity score (for the trauma patients, this was the MODS score; forthe burn patients, this was the Denver score). Death type was enumeratedas per the original definitions by the Glue Grant authors: either (1)sepsis death, (2) traumatic brain injury (TBI)/brain death, or (3) otherdeath. We split out TBI/brain death as these deaths are often primarilydirect sequelae of the initial injury, rather than being caused by hostresponse. In the burns cohort, several patients were noted to die duringthe study period but after 30 days; these patients were excluded. Wethen performed two types of ROC analysis. In the first, we examinedpatients who contracted sepsis who eventually died, and compared themwith patients who contracted sepsis but did not die, but only thosepatients who contracted sepsis within the same time window wereincluded. This is analogous to examining only the day of admission inthe community-acquired sepsis cohorts. In the second ROC analysis, weexamined all patients, comparing patients who died of any cause to thosewho did not. Here we split the groups into bins by time since admission;however, some patients were thus repeated between these groups, and sofor the all-cause mortality analysis, the different time-bins are notindependent.

Cell-Type Enrichment Tests

Gene sets were evaluated for enrichment in previously examined in vitroimmune cell profiles as previously described¹⁵. Briefly, GEO wassearched for gene expression profiles of clinical samples of relevantimmune cell types. For multiple samples all corresponding to the samecell type, the mean of the samples was taken as the final value, thuscreating a single vector for each cell type. Gene scores for each geneset were calculated in the resulting cell-type vectors as describedabove. These scores are then standardized across all cell types, suchthat the score represents the number of standard deviations away fromthe group mean. This thus represents how enriched a given gene set is ina given cell type, relative to other tested cell types.

Two gene sets were tested in this manner: both the entire set of genesfound to be significant after the multi-cohort/meta-regress analysis,and the subset of genes found to be most diagnostic after the iteratedgreedy search. Resulting plots show the Z-score (enrichment for thegiven gene set) in each cell subtype (black dots), as well as a box plotfor the overall distribution of Z-scores (shown in red).

Statistics and R

All computation and calculations were carried out in the R language forstatistical computing (version 3.2.0). Significance levels for p-valueswere set at 0.05, and analyses were two-tailed, unless specifiedotherwise.

Results

Analysis Overview

Our systematic search revealed 19 cohorts that matchedinclusion/exclusion criteria^(9,11,12,14,15,19-23,26,34-38). Of these,we prospectively identified two datasets as validation cohorts: GSE54514and E-MTAB-4421.51. The other validation datasets (GSE21802, GSE33341,GSE63990) only had the phenotype data become available after analysiswas completed. Thus, in the remaining datasets, we took all those whichspecifically examined sepsis patients at admission to the hospital or tothe ICU, which yielded 12 cohorts, with a total of 490 survivors and 160non-survivors (Table 1). We applied two analytic methods to discovergenes significantly associated with mortality (FIG. 1). In the first, weperformed multi-cohort meta-analysis for differential gene expressionbetween survivors and non-survivors at admission, yielding 96 genessignificant at FDR<0.05 and effect size >1.3-fold. In the secondanalysis, we performed synthesis-of-slopes random-effectsmeta-regression for mortality as a logistic function of clinicalseverity, age, and gene expression. This yielded 35 genes significant atp<0.01. Notably, the top three most-significant genes in themeta-regression were all from the same pathway, namely, neutrophilazurophilic granules: DEFA4, CTSG, and MPO. The union of themeta-analysis and meta-regression gene sets was 122 genes, which we tookas our ‘significant’ gene list.

Discovery of the 12-Gene Set

We next used the 122-gene list to perform an iterated greedy search onthe 12 discovery datasets, trying to find a gene list which maximizeddiagnostic performance, as measured by weighted AUC. Briefly, thealgorithm iterates between a forward and backward greedy search, untilit converges on a gene list. This algorithm is designed to find maximacloser to the global maximum than a simple forward search. The algorithmran to completion, producing a 12-gene set. The genes upregulated inpatients with mortality were: DEFA4, CD163, PER1, RGS1, HIF1A, SEPP1,C11orf74, and CIT, and the downregulated genes were: LY86, TST, OR52R1,and KCNJ2. This 12-gene set had a mean discovery AUC of 0.847+/−0.081,with a summary AUC of 0.85 (FIGS. 2-6). In addition, in comparing themortality gene score with the clinical severity score in the discoverycohorts, we showed that in all cases, the gene score improved upon theprediction of mortality using clinical severity alone (except in twocases where clinical severity perfectly separated mortality; Table 2).Finally, only one cohort (GSE63042) contained blood lactate levels; inthis group, the blood lactate AUC was 0.72, while the 12-gene score AUCwas 0.78.

Direct Validation in Sepsis Patients at Admission

We next examined the prognostic power of the 12-gene score in the 5validation datasets that examined sepsis patients at admission (Table3). For the two cohorts which had longitudinal samples (GSE21802 andGSE54514), we examined only the first 48 or 24 hours after admission,respectively, without repeating any patients within the analysis. Thisthus yielded a total of 415 survivors and 136 non-survivors. Acrossthese five cohorts, the 12-gene score showed a summary AUC of 0.84 (95%CI 0.58-0.96; FIGS. 7-9). Notably, the AUC was lowest in E-MTAB-4421.51;this cohort allowed patients to be enrolled in a window up to day 5after admission, possibly biasing the results. In analysis of the twovalidation cohorts with longitudinal sampling (GSE21802, GSE54514), wesaw similar trends, namely, a roughly preserved AUC over time, untilsurvivor bias in the late time-points caused a drop in AUC (FIGS.10-11). In addition, in both cohorts, the 12-gene score showed a generaltrend in decrease over time, meaning that a sample drawn at 5 days aftersepsis diagnosis would have a lower expected score than a sample drawnat day 1. Such a finding may support a downward bias due to samplingtechnique in E-MTAB-4421.51. In addition, both GSE21802 and GSE54514included admission severity scores; we again compared prognostic poweralone and in combination with the 12-gene score, showing a small lift(0.02-0.05) in both cases.

Validation in the Glue Grant

We next examined the Glue Grant trauma (buffy coat) and burns (wholeblood) patients (Table 3). For the trauma cohorts, we first plottedtrajectories of all patients for both 12-gene score and clinicalseverity score (FIG. 12), showing a clear trend of higher gene scores inthe mortality cases. In the one defined sepsis death in this cohort, thepatient's 12-gene scores are higher than all other scores at three offour timepoints. We also examined prognostic power using ROC curves,both for sepsis patients at time of diagnosis and for all patients overtime since admission. The sepsis patients matched for time of diagnosisshowed only one death, but this was perfectly predicted compared to theother 12 patients (FIG. 13A). The 12-gene score also showed moderateprognostic power for all-cause mortality among all patients over time(AUC 0.75-0.86, FIG. 13B). In the Glue Grant burns cohort, we performedthe same type of analysis, with similar outcomes. Gene scoretrajectories of patients who died were generally separable from thosewho lived, though again clinical severity trajectories did a similarlygood job of identifying these patients (FIG. 14). Examining time-matchedpatients with sepsis, predicting diagnosis, the 12-gene score had poorpower at day 1-day 3 post-admission (AUC 0.63), but a much higher powerat day 3-day 7 (AUC 0.85); these two time bins contained no overlappingpatients (FIG. 15A). Examining the prediction of all-cause mortalitylongitudinally, the burns data showed a range of AUCs (0.63-0.82),showing an increased over time since admission, with particularly poorperformance in the first 24 hours since burn (FIG. 15B).

Cell-Type-Specific Expression

Finally, to gain some insight into the biology of these genes, weperformed in silico cell-type expression analysis of both all 122significant genes, as well as the prognostic 12-gene set, as previouslydescribed (FIG. 16). Here the hypothesis is that an increase in signalwithin a specific immune cell type may suggest that the change inexpression of these genes in whole blood may in fact signal a detectionof a cell-type shift, instead of (or in addition to) changes inintracellular transcript levels. Intriguingly, the 122-gene set showedsignificant enrichment in bands and metamyelocytes, indicating thatimmature neutrophils may be specifically linked to sepsis mortality, inline with some prior suggestions by other groups. However, the 12-geneset did not show any significant over-enrichment in any cell types. Thenegative signal found for centrocytes is unlikely to be biologicallyrelevant, since these cell types are not present in the blood inmeaningful quantities.

Discussion

Sepsis is an incredibly heterogeneous syndrome, including a widepossible range of patient conditions, comorbidities, acute severity,time since infections, and underlying immune states. Many havepreviously hypothesized that a molecular profile of the immune systemmay be able to predict sepsis outcomes. However, the best clinical toolsfor predicting outcomes remain blood lactate and clinical severityscores, both of which have several limitations. Here, we combined alarge amount of data (12 cohorts, 650 patients) to define a set of just12 genes that is able to predict mortality in septic patients. Wevalidated this score directly in 5 cohorts with 551 patients, with asummary ROC AUC of 0.84. In addition, we showed in additional 421patients admitted with severe trauma or burns that the 12-gene score haspreserved predictive power for mortality in patients with sepsis, aswell as some ability to predict all-cause mortality in very broadcohorts.

This molecular definition of the severity of the host response in sepsisis important for several reasons. First, better sepsis prognosis canimprove clinical care by correctly matching patients with resources:sicker patients can be diverted to ICU with maximal intervention, whilepatients unlikely to die may be safely watched in the hospital ward.Second, both patients and clinicians can benefit from more-preciseestimates of prognosis, allowing for better discussions of patientpreferences and the choice to intervene. Finally, better molecularphenotyping of sepsis patients can allow for great improvements inclinical trials, through both (1) patient selection and prognosticenrichment for drugs and interventions, and (2) better assessments ofobserved-to-expected ratios for mortality^(5,6).

The genes we have identified as being associated with sepsis mortalitymay also denote important underlying biology. For instance, the topthree genes associated with mortality independent of clinical severitywere azurophilic granule proteases, indicating a presence of veryimmature neutrophils (metamyelocytes) in the blood. Whether theseproteases are themselves harmful, or are markers of harmful cell-typeshifts, remains to be confirmed. Several groups have shown that latesepsis deaths are mostly due to an ‘immune paralysis’ that affects theadaptive immune system. The transcriptomic changes shown here may bethus part of a cascade of events that is indirectly associated withmortality through induction of adaptive immune collapse. These genes maythus be direct therapeutic targets, in addition to simply being markersfor sepsis severity. More study is needed.

Our analysis has some weaknesses; most notably, the largest validationdataset, E-MTAB-4421.51 shows the worst performance of all datasets.There are at least three possible explanations for this: first, asdescribed, this cohort allowed inclusion up to day five since admission,but did not include which day was associated with which patient in thepublic phenotype file. Since the score appears to fall over time inpatients admitted with sepsis, and since there may be a bias towardssicker patients being enrolled later (as it is often difficult to enrollpatients who are very sick at admission), this could lead to a downwardbias. Second, it is possible that this cohort is ‘sicker’ than the othercohorts, although the mean APACHE scores of 18-23 are nearly identicalto those of, say GSE54514, in which our diagnostic power was confirmed.Finally, of course it is possible that the gene set may simply notperform well in this cohort either due to its size or some otherclinical factors that are unknown. This seems less likely given that inthe 16 other discovery and validation cohorts, the lowest AUC is 0.72(GSE10474, N=33). In any case, further prospective confirmation isnecessary prior to clinical application.

Overall, through integrated analysis of the available transcriptomicdata in sepsis, we have derived a set of 12 genes whose levels can beused to predict short-term (30-day) mortality in sepsis patients, withconfirmation across 551 independent patients from 5 cohorts. Thesefindings indicate that the whole blood transcriptome does carry an earlysignal for eventual mortality. They also illustrate a set of targets forfurther mechanistic or therapeutic study in severe sepsis. Finally, the12-gene set could potentially be used directly as a prognostic test,though further prospective confirmation is necessary.

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While the preferred embodiments of the invention have been illustratedand described, it will be appreciated that various changes can be madetherein without departing from the spirit and scope of the invention.

What is claimed is:
 1. A method administering urgent care to a patient,comprising: a) obtaining a biological sample from the patient; b)detecting levels of expression of DEFA4, CD163, PER1, RGS1, HIF1A,SEPP1, C11orf74, CIT, LY86, TST, and KCNJ2 biomarkers in the biologicalsample; c) identifying the patient as having a high risk of mortalitywithin 30 days, wherein the patient has increased levels of expressionof the DEFA4, CD163, PER1, RGS1, HIF1A, SEPP1, C11orf74, and CITbiomarkers and decreased levels of expression of the LY86, TST, andKCNJ2 biomarkers compared to reference value ranges; and d)administering an antimicrobial therapy to the patient, administering animmune-modulating therapy to the patient, or administering anorgan-specific treatment to the patient.
 2. The method of claim 1,wherein detecting the levels of expression of the biomarkers comprisesperforming microarray analysis, polymerase chain reaction (PCR), reversetranscriptase polymerase chain reaction (RT-PCR), a Northern blot, aserial analysis of gene expression (SAGE), or isothermal amplification.3. The method of claim 1, wherein the biological sample comprises blood,buffy coat, band cells, or metamyelocytes.
 4. The method of claim 1,wherein the levels of the biomarkers are compared to time-matchedreference values for infected or non-infected subjects.
 5. The method ofclaim 1, wherein the organ-specific treatment comprises either or bothof connecting the patient to any one or more of a mechanical ventilator,a pacemaker, a defibrillator, a dialysis or renal replacement therapymachine, an invasive monitor including a pulmonary artery catheter,arterial blood pressure catheter, or central venous pressure catheter,or administering blood products, vasopressors, or sedatives.
 6. A kitcomprising agents for detecting the levels of expression of up to 30biomarkers, wherein the up to 30 biomarkers comprise DEFA4, CD163, PER1,RGS1, HIF1A, SEPP1, C11orf74, CIT, LY86, TST, and KCNJ2 transcripts. 7.The kit of claim 6, wherein the kit comprises a microarray.
 8. The kitof claim 7, further comprising information, in electronic or paper form,comprising instructions to correlate the detected levels of thebiomarkers with mortality risk.
 9. The kit of 50, wherein the kitcomprises: an oligonucleotide that hybridizes to a DEFA4 polynucleotide,an oligonucleotide that hybridizes to a CD163 polynucleotide, anoligonucleotide that hybridizes to a PER1 polynucleotide, anoligonucleotide that hybridizes to a RGS1 polynucleotide, anoligonucleotide that hybridizes to an HIF1A polynucleotide, anoligonucleotide that hybridizes to a SEPP1 polynucleotide, anoligonucleotide that hybridizes to a C11orf74 polynucleotide, anoligonucleotide that hybridizes to a CIT polynucleotide, anoligonucleotide that hybridizes to a LY86 polynucleotide, anoligonucleotide that hybridizes to a TST polynucleotide, and anoligonucleotide that hybridizes to a KCNJ2 polynucleotide.